Detecting repeat expansions with short read sequencing data

ABSTRACT

The disclosed embodiments concern methods, apparatus, systems and computer program products for determining the presence or absence of repeat expansions of interest, including repeat expansions of repeat sequences that are medically significant. Some embodiments provide methods for identifying and calling medically relevant repeat expansions using anchored reads. An anchored read is a paired end read that is unaligned to a repeat sequence under consideration, but it is paired with an anchor read that is aligned to or near the repeat sequence. Some embodiments use both anchor and anchored reads to determine the presence or absence of the repeat expansions. System, apparatus, and computer program products are also provided for determining repeat expansion implementing the methods disclosed.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims benefits under 35 U.S.C. §119(e) to U.S.Provisional Patent Application No. 62/049,925, entitled: DETECTINGREPEAT EXPANSIONS WITH SHORT READ SEQUENCING DATA, filed Sep. 12, 2014,which is herein incorporated by reference in its entirety for allpurposes.

BACKGROUND

Repeat expansions are a special class of microsatellite andminisatellite variants involving short tandem repeat (STR)polymorphisms. Repeat expansions are also known as dynamic mutations dueto their instability when short tandem repeats expand beyond certainsizes. Genetic disorders caused by unstable repeat expansions include,among others, fragile X syndrome (FXS), Huntington's disease, andamyotrophic lateral sclerosis (ALS).

Identifying repeat expansions is important in the diagnosis andtreatment of certain genetic disorders. However, it is difficult todetermine repeat sequences using short reads that do not fully traversethe repeat sequence. Therefore, it is desirable to develop methods usingshort reads to identify medically relevant repeat expansions.

SUMMARY

The disclosed implementations concern methods, apparatus, systems, andcomputer program products for determining repeat expansions of interest,such as expansions of repeat sequences that are related to geneticdisorders. The disclosed implementations use paired end sequencing.Methods to detect repeats of one or more repeat units in a local geneticregion are provided. If a local region of a test sample has more repeatsin it than an unaffected population, the test sample may be identifiedas having the repeat expansion under consideration.

A first aspect of the disclosure provides methods for identifying andcalling medically relevant repeat expansions using anchored reads. Someembodiments use both anchor and anchored reads to determine the presenceor absence of the repeat expansions. In one example, a method isprovided for determining the presence or absence of a repeat expansionof a repeat sequence in a test sample including nucleic acids, where therepeat sequence includes repeats of a repeat unit of nucleotides. Themethod involves (a) obtaining paired end reads of the test sample, wherethe paired end reads have been processed to align to the referencesequence including the repeat sequence. The method further involves (b)identifying anchor and anchored reads in the paired end reads, where theanchor reads are reads aligned to or near the repeat sequence, and theanchored reads are unaligned reads that are paired with the anchorreads. The method also involves (c) determining if the repeat expansionis likely to be present in the test sample based at least in part on theidentified anchored reads. In some implementations, determining if therepeat expansion is likely to be present is based on the identifiedanchor reads and the identified anchored reads. In some implementations,determining if the repeat expansion is likely to be present is alsobased on numbers of repeats of the repeat unit in the identified reads.

In some implementations, determining if the repeat expansion is likelyto be present involves: obtaining the number of identified reads thatare high-count reads, where the high-count reads include reads havingmore repeats than a threshold value; and comparing the number ofhigh-count reads in the test sample to a call criterion. In someimplementations, the threshold value for high-count reads is at leastabout 80% of the maximum number of repeats, which maximum is calculatedfrom the length of the paired end reads and the length of the repeatunit. In some implementations, the threshold value for high-count readsis at least about 90% of the maximum number of repeats. In someimplementations, the call criterion is obtained from a distribution ofhigh-count reads of control samples. In some implementations, the callcriterion is calculated from the length of the paired end reads, alength of a sequence having the repeat expansion, and a sequencingdepth. In some implementations, the call criterion is calculated fromthe distance between the first and last observation of the repeatsequence within the reads.

In some implementations, the anchor reads are aligned to or within about5 kb of the repeat sequence. In some implementations, the anchor readsare aligned to or within about 1 kb of the repeat sequence. In someimplementations, the unaligned reads include reads that cannot bealigned or are poorly aligned to the reference sequence.

In some implementations, the reference sequence includes a referencegenome. In some implementations, the methods further involve determiningthat the individual from whom the test sample is obtained has anelevated risk of one of Fragile X syndrome, amyotrophic lateralsclerosis (ALS), Huntington's disease, Friedreich's ataxia,spinocerebellar ataxia, spino-bulbar muscular atrophy, myotonicdystrophy, Machado-Joseph disease, or dentatorubral pallidoluysianatrophy.

In some implementations, (c) includes comparing a distribution of thenumbers of repeats of the repeat unit in the identified reads for thetest sample and a distribution of numbers of repeats for one or morecontrol samples. In some implementations, comparing the distribution forthe test sample to the distribution for the control samples includesusing a Mann-Whitney rank test to determine if the distribution of thetest sample statistically significantly differs from the distribution ofthe control samples. In some implementations, the method furtherinvolves determining that the repeat expansion is likely present in thetest sample if the test sample's distribution is skewed more towardshigher numbers of repeats than the control samples, and the p value forthe Mann-Whitney rank test is smaller than about 0.0001. In someimplementations, the method further involves determining that the repeatexpansion is likely present in the test sample if the test sample'sdistribution is skewed more towards higher numbers of repeats than thecontrol samples, and the p value for the Mann-Whitney rank test issmaller than about 0.00001.

In some implementations, the method further involves using a sequencerto generate paired end reads from the test sample. In someimplementations, the method further includes extracting the test samplefrom an individual.

In some implementations, the numbers of repeats are numbers of in-framerepeats. In some implementations, the test sample is a blood sample, aurine sample, a saliva sample, or a tissue sample. In someimplementations, the test sample includes fetal and maternal cell-freenucleic acids. In some implementations, the repeat unit includes 2 to 50nucleotides.

In some implementations, the paired end reads are shorter than a repeatsequence having the repeat expansion. In some implementations, thepaired end reads include reads of about 20 bp to 1000 bp. In someimplementations, the paired end reads include reads of about 50 bp to500 bp, or reads of about 80 bp to 150 bp. In some implementations, asequence having the repeat expansion is longer than about 100 bp. Insome implementations, the sequence having the repeat expansion is longerthan about 500 bp. In some implementations, the sequence having therepeat expansion is longer than about 1000 bp.

In some implementations, the paired end reads are obtained from insertsof about 100-5000 bp. In some implementations, the inserts are about100-1000 bp long. In some implementations, the inserts are about1000-5000 bp long.

A second aspect of the disclosure provides methods for detecting arepeat expansion in a test sample including nucleic acids. In someimplementations, the method involves: (a) obtaining paired end reads ofthe test sample; (b) aligning the paired end reads to a referencegenome; (c) identifying unaligned reads from the whole genome, whereinthe unaligned reads include paired end reads that cannot be aligned orare poorly aligned to the reference sequence; and (d) analyzing thenumbers of repeats of a repeat unit in the unaligned reads to determineif a repeat expansion is likely present in the test sample. In someimplementations, analyzing the numbers of repeats of the repeat unit inthe unaligned reads includes: obtaining the number of high-count reads,wherein the high-count reads include unaligned reads having more repeatsthan a threshold value; and comparing the number of high-count reads inthe test sample to a call criterion. In some implementations, thethreshold value for high-count reads is at least about 80% of themaximum number of repeats, which maximum is calculated as the ratio ofthe length of the paired end reads over the length of the repeat unit.In some implementations, the high-count reads further include reads thatare paired to the unaligned reads and have more repeats than thethreshold value.

In some implementations, a method further involves, upon determinationthat the repeat expansion is likely present in the test sample,performing an additional analysis to determining if the test sampleincludes a repeat expansion of a particular repeat sequence of interest.In some implementations, the additional analysis includes assaying thetest sample using reads longer than the paired end reads. In someimplementations, the additional analysis includes assaying the testsample using single molecule sequencing or synthetic long-readsequencing.

In some implementations, the method further involves, prior toperforming the additional analysis, identifying paired end reads thatare paired to the unaligned reads and are aligned to or near a repeatsequence on the reference genome; and providing the repeat sequence asthe particular repeat sequence of interest. In some implementations, theadditional analysis includes an analysis using the method of any methodsof the first aspect of the disclosure.

Another aspect of the disclosure provides systems for determining thepresence or absence of a repeat expansion of a repeat sequence in a testsample including nucleic acids, where the repeat sequence includesrepeats of a repeat unit. In some implementations, the system involves:a sequencer for sequencing nucleic acids of the test sample; aprocessor; and one or more computer-readable storage media having storedthereon instructions for execution on said processor to evaluate copynumber in the test sample. The instructions includes: (a) aligningpaired end reads to a reference sequence including the repeat sequence;(b) identifying anchor and anchored reads in the paired end reads,wherein the anchor reads are reads aligned to or near the repeatsequence, and the anchored reads are unaligned reads that are pairedwith the anchor reads; and (c) determining if the repeat expansion islikely to be present in the test sample based at least in part on theidentified anchored reads. In some implementations, (c) includesdetermining if the repeat expansion is likely to be present in the testsample based at least in part on the numbers of repeats of the repeatunit in the identified reads. In some implementations, (c) includes:obtaining the number of identified reads that are high-count reads,wherein the high-count reads include reads having more repeats than athreshold value; and comparing the number of high-count reads in thetest sample to a call criterion.

Another aspect of the disclosure provides a computer program productincluding a non-transitory machine readable medium storing program codethat, when executed by one or more processors of a computer system,causes the computer system to implement a method for identifying arepeat expansion of a repeat sequence in a test sample including nucleicacids, wherein the repeat sequence includes repeats of a repeat unit ofnucleotides, said program code including: (a) code for obtaining pairedend reads of the test sample that have been processed to align to areference sequence including the repeat sequence; (b) code foridentifying anchor and anchored reads in the paired end reads, whereinthe anchor reads are reads aligned to or near the repeat sequence, andthe anchored reads are unaligned reads that are paired with the anchorreads; and (c) code for determining if the repeat expansion is likely tobe present in the test sample based at least in part on the identifiedanchored reads. In some implementations, (c) includes code for analyzingthe numbers of repeats of the repeat unit in the identified reads. Insome implementations, (c) includes: code for obtaining the numberidentified reads that are high-count reads, wherein the high-count readsinclude reads having more repeats than a threshold value; and code forcomparing the number of high-count reads in the test sample to a callcriterion.

Although the examples herein concern humans and the language isprimarily directed to human concerns, the concepts described herein areapplicable to genomes from any plant or animal. These and other objectsand features of the present disclosure will become more fully apparentfrom the following description and appended claims, or may be learned bythe practice of the disclosure as set forth hereinafter.

INCORPORATION BY REFERENCE

All patents, patent applications, and other publications, including allsequences disclosed within these references, referred to herein areexpressly incorporated herein by reference, to the same extent as ifeach individual publication, patent or patent application wasspecifically and individually indicated to be incorporated by reference.All documents cited are, in relevant part, incorporated herein byreference in their entireties for the purposes indicated by the contextof their citation herein. However, the citation of any document is notto be construed as an admission that it is prior art with respect to thepresent disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a schematic diagram illustrating difficulties in alignment ofsequence reads to a repeat sequence on a reference sequence.

FIG. 1B is a schematic diagram illustrating alignment of sequence readsusing paired end reads according to certain disclosed implementations toovercome the difficulties shown in FIG. 1A.

FIG. 2 is a flow diagram providing a high level depiction of an exampleof a method for determining the presence or absence of an expansion of arepeat sequence in a sample.

FIGS. 3 and 4 are flow diagrams illustrating examples of methods fordetecting a repeat expansion using paired end reads.

FIG. 5 is a flow diagram of a method that uses unaligned reads notassociated with any repeat sequence of interest to determine a repeatexpansion.

FIG. 6 is a block diagram of a dispersed system for processing a testsample.

FIG. 7 shows the distribution of CGG triplet counts in paired end readsaligned or anchored to the FMR1 gene from 1013 control samples.

FIG. 8 shows a distribution of p values of the Mann-Whitney (MW) ranktest for the control samples.

FIG. 9 shows the distribution of the numbers of repeats of the samplehaving the highest MW rank test score and the lowest p-value.

FIG. 10 shows data for a female patient sample known to have the repeatexpansion of the FMR1 gene and fragile X syndrome.

FIG. 11 shows data for a male Fragile X patient sample having 645 copiesof the CGG triplet on the X chromosome.

FIG. 12 shows the same distribution of p values of the Mann-Whitney ranktest for the control samples as FIG. 8, with the additional indicationof four of the highest scoring female samples and four of the highestscoring male samples.

FIG. 13 shows the numbers of samples having various numbers ofhigh-count reads, including samples having Fragile X syndrome shown inhatched bars.

FIG. 14 shows the theoretical simulated distribution of the expectednumber of reads fully within a repeat sequence of 60 triplets.

FIG. 15 shows the mean, 5^(th) percentile and 95^(th) percentile ofexpected number of reads fully in the repeat sequence having variousnumbers of triplets based on simulations with the same experimentalconditions of FIG. 14.

FIG. 16 shows the same data as FIG. 15, while identifying theobservation of having 20 reads fully in the repeat sequence.

FIG. 17 shows the numbers of samples having various numbers ofhigh-count reads, including samples having amyotrophic lateral sclerosis(ALS) shown in hatched bars.

DETAILED DESCRIPTION

The disclosure concerns methods, apparatus, systems, and computerprogram products for identifying repeat expansions of interest, such asexpansions of repeat sequences that are medically significant. Examplesof repeat expansions include but are not limited to expansionsassociated with genetic disorders such as Fragile X syndrome, ALS,Huntington's disease, Friedreich's ataxia, spinocerebellar ataxia,spino-bulbar muscular atrophy, myotonic dystrophy, Machado-Josephdisease, and dentatorubral pallidoluysian atrophy.

Unless otherwise indicated, the practice of the methods and systemsdisclosed herein involves conventional techniques and apparatus commonlyused in molecular biology, microbiology, protein purification, proteinengineering, protein and DNA sequencing, and recombinant DNA fields thatare within the skill of the art. Such techniques and apparatus are knownto those of skill in the art and are described in numerous texts andreference works (See e.g., Sambrook et al., “Molecular Cloning: ALaboratory Manual,” Third Edition (Cold Spring Harbor), [2001]); andAusubel et al., “Current Protocols in Molecular Biology” [1987]).

Numeric ranges are inclusive of the numbers defining the range. It isintended that every maximum numerical limitation given throughout thisspecification includes every lower numerical limitation, as if suchlower numerical limitations were expressly written herein. Every minimumnumerical limitation given throughout this specification will includeevery higher numerical limitation, as if such higher numericallimitations were expressly written herein. Every numerical range giventhroughout this specification will include every narrower numericalrange that falls within such broader numerical range, as if suchnarrower numerical ranges were all expressly written herein.

The headings provided herein are not intended to limit the disclosure.

Unless defined otherwise herein, all technical and scientific terms usedherein have the same meaning as commonly understood by one of ordinaryskill in the art. Various scientific dictionaries that include the termsincluded herein are well known and available to those in the art.Although any methods and materials similar or equivalent to thosedescribed herein find use in the practice or testing of the embodimentsdisclosed herein, some methods and materials are described.

The terms defined immediately below are more fully described byreference to the Specification as a whole. It is to be understood thatthis disclosure is not limited to the particular methodology, protocols,and reagents described, as these may vary, depending upon the contextthey are used by those of skill in the art.

Definitions

As used herein, the singular terms “a,” “an,” and “the” include theplural reference unless the context clearly indicates otherwise.

Unless otherwise indicated, nucleic acids are written left to right in5′ to 3′ orientation and amino acid sequences are written left to rightin amino to carboxy orientation, respectively.

The term “plurality” refers to more than one element. For example, theterm is used herein in reference to a number of nucleic acid moleculesor sequence reads that is sufficient to identify significant differencesin repeat expansions in test samples and control samples using themethods disclosed herein.

The term “repeat sequence” refers to a longer nucleic acid sequenceincluding repetitive occurrences of a shorter sequence. The shortersequence is referred to as a “repeat unit” herein. The repetitiveoccurrences of the repeat unit are referred to as “repeats” or “copies”of the repeat unit. In many contexts, a repeat sequence is associatedwith a gene encoding a protein. In other situations, a repeat sequencemay be in a non-coding region. The repeat units may occur in the repeatsequence with or without breaks between the repeat units. For instance,in normal samples, the FMR1 gene tends to include an AGG break in theCGG repeats, e.g., (CGG)10+(AGG)+(CGG)9. Samples lacking a break, aswell as long repeat sequences having few breaks, are prone to repeatexpansion of the associated gene, which can lead to genetic diseases asthe repeats expand above a particular number. In various embodiments ofthe disclosure, the number of repeats is counted as in-frame repeatsregardless of breaks. Methods for estimating in-frame repeats arefurther described hereinafter.

In various embodiments, the repeat units include 2 to 100 nucleotides.Many repeat units widely studied are trinucleotide or hexanucleotideunits. Some other repeat units that have been well studied and areapplicable to the embodiments disclosed herein include but are notlimited to units of 4, 5, 6, 8, 12, 33, or 42 nucleotides. See, e.g.,Richards (2001) Human Molecular Genetics, Vol. 10, No. 20, 2187-2194.Applications of the disclosure are not limited to the specific number ofnucleotide bases described above, so long as they are relatively shortcompared to the repeat sequence having multiple repeats or copies of therepeat units. For example, a repeat unit can include at least 3, 6, 8,10, 15, 20, 30, 40, 50 nucleotides. Alternatively or additionally, arepeat unit can include at most about 100, 90, 80, 70, 60, 50, 40, 30,20, 10, 6 or 3 nucleotides.

A repeat sequence may be expanded in evolution, development, andmutagenic conditions, creating more copies of the same repeat unit. Thisis referred to as “repeat expansion” in the field. This process is alsoreferred to as “dynamic mutation” due to the unstable nature of theexpansion of the repeat unit. Some repeat expansions have been shown tobe associated with genetic disorders and pathological symptoms. Otherrepeat expansions are not well understood or studied. The disclosedmethods herein may be used to identify both previously known and newrepeat expansions. In some embodiments, a repeat sequence having arepeat expansion is longer than about 500 base pairs (bp). In someembodiments, a repeat sequence having the repeat expansion is longerthan about 1000 bp, 2000 bp, 3000 bp, 4000 bp, or 5000 bp, etc.

The term “paired end reads” refers to reads obtained from paired endsequencing that obtains one read from each end of a nucleic fragment.Paired end sequencing involves fragmenting DNA into sequences calledinserts. In some protocols such as some used by Illumina, the reads fromshorter inserts (e.g., on the order of tens to hundreds of bp) arereferred to as short-insert paired end reads or simply paired end reads.In contrast, the reads from longer inserts (e.g., on the order ofseveral thousands of bp) are referred to as mate pair reads. In thisdisclosure, short-insert paired end reads and long-insert mate pairreads may both be used and are not differentiated with regard to theprocess for analyzing repeat expansions. Therefore, the term “paired endreads” may refer to both short-insert paired end reads and long-insertmate pair reads, which are further described herein after. In someembodiments, paired end reads include reads of about 20 bp to 1000 bp.In some embodiments, paired end reads include reads of about 50 bp to500 bp, about 80 bp to 150 bp, or about 100 bp. It will be understoodthat the two reads in a paired end need not be located at the extremeend of the fragment that is sequenced. Rather, one or both read can beproximate to the end of the fragment. Furthermore, methods exemplifiedherein in the context of paired end reads can be carried out with any ofa variety of paired reads independent of whether the reads are derivedfrom the end of a fragment or other part of a fragment.

As used herein, the terms “alignment” and “aligning” refer to theprocess of comparing a read to a reference sequence and therebydetermining whether the reference sequence contains the read sequence.An alignment process attempts to determine if a read can be mapped to areference sequence, but does not always result in a read aligned to thereference sequence. If the reference sequence contains the read, theread may be mapped to the reference sequence or, in certain embodiments,to a particular location in the reference sequence. In some cases,alignment simply tells whether or not a read is a member of a particularreference sequence (i.e., whether the read is present or absent in thereference sequence). For example, the alignment of a read to thereference sequence for human chromosome 13 will tell whether the read ispresent in the reference sequence for chromosome 13. A tool thatprovides this information may be called a set membership tester. In somecases, an alignment additionally indicates a location in the referencesequence where the read maps to. For example, if the reference sequenceis the whole human genome sequence, an alignment may indicate that aread is present on chromosome 13, and may further indicate that the readis on a particular strand and/or site of chromosome 13.

Aligned reads are one or more sequences that are identified as a matchin terms of the order of their nucleic acid molecules to a knownreference sequence such as a reference genome. An aligned read and itsdetermined location on the reference sequence constitute a sequence tag.Alignment can be done manually, although it is typically implemented bya computer algorithm, as it would be impossible to align reads in areasonable time period for implementing the methods disclosed herein.One example of an algorithm from aligning sequences is the EfficientLocal Alignment of Nucleotide Data (ELAND) computer program distributedas part of the Illumina Genomics Analysis pipeline. Alternatively, aBloom filter or similar set membership tester may be employed to alignreads to reference genomes. See U.S. patent application Ser. No.14/354,528, filed Apr. 25, 2014, which is incorporated herein byreference in its entirety. The matching of a sequence read in aligningcan be a 100% sequence match or less than 100% (i.e., a non-perfectmatch).

The term “mapping” used herein refers to assigning a read sequence to alarger sequence, e.g., a reference genome, by alignment.

In some instances one end read of two paired end reads is aligned to arepeat sequence of a reference sequence, while the other end read of thetwo paired end reads is unaligned. In such instances, the paired readthat is aligned to a repeat sequence of a reference sequence is referredto as an “anchor read.” A paired end read unaligned to the repeatsequence but is paired with the anchor read is referred to as ananchored read. As such, an unaligned read can be anchored to andassociated with the repeat sequence. In some embodiments, the unalignedreads include both reads that cannot be aligned to the referencesequence and reads that are poorly aligned to a reference sequence. Whena read is aligned to a reference sequence with a number of mismatchedbases above a certain criterion, the read is considered poorly aligned.For example, in various embodiments, a read is considered poorly alignedwhen it is aligned with at least about 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10mismatches. In some instances, both reads of a pair are aligned to areference sequence. In such instances, both reads may be analyzed as“anchor reads” in various implementations.

The terms “polynucleotide,” “nucleic acid” and “nucleic acid molecules”are used interchangeably and refer to a covalently linked sequence ofnucleotides (i.e., ribonucleotides for RNA and deoxyribonucleotides forDNA) in which the 3′ position of the pentose of one nucleotide is joinedby a phosphodiester group to the 5′ position of the pentose of the next.The nucleotides include sequences of any form of nucleic acid,including, but not limited to RNA and DNA molecules such as cell-freeDNA (cfDNA) molecules. The term “polynucleotide” includes, withoutlimitation, single- and double-stranded polynucleotides.

The term “test sample” herein refers to a sample, typically derived froma biological fluid, cell, tissue, organ, or organism, that includes anucleic acid or a mixture of nucleic acids having at least one nucleicacid sequence that is to be screened for copy number variation. Incertain embodiments the sample has at least one nucleic acid sequencewhose copy number is suspected of having undergone variation. Suchsamples include, but are not limited to sputum/oral fluid, amnioticfluid, blood, a blood fraction, or fine needle biopsy samples, urine,peritoneal fluid, pleural fluid, and the like. Although the sample isoften taken from a human subject (e.g., a patient), the assays can beused to copy number variations (CNVs) in samples from any mammal,including, but not limited to dogs, cats, horses, goats, sheep, cattle,pigs, etc. The sample may be used directly as obtained from thebiological source or following a pretreatment to modify the character ofthe sample. For example, such pretreatment may include preparing plasmafrom blood, diluting viscous fluids, and so forth. Methods ofpretreatment may also involve, but are not limited to, filtration,precipitation, dilution, distillation, mixing, centrifugation, freezing,lyophilization, concentration, amplification, nucleic acidfragmentation, inactivation of interfering components, the addition ofreagents, lysing, etc. If such methods of pretreatment are employed withrespect to the sample, such pretreatment methods are typically such thatthe nucleic acid(s) of interest remain in the test sample, sometimes ata concentration proportional to that in an untreated test sample (e.g.,namely, a sample that is not subjected to any such pretreatmentmethod(s)). Such “treated” or “processed” samples are still consideredto be biological “test” samples with respect to the methods describedherein.

A control sample may be a negative or positive control sample. A“negative control sample” or “unaffected sample” refers to a sampleincluding nucleic acids that is known or expected to have a repeatsequence having a number of repeats within a range that is notpathogenic. A “positive control sample” or “affected sample” is known orexpected to have a repeat sequence having a number of repeats within arange that is pathogenic. Repeats of the repeat sequence in a negativecontrol sample typically have not been expanded beyond a normal range,whereas repeats of a repeat sequence in a positive control sampletypically have been expanded beyond a normal range. As such, the nucleicacids in a test sample can be compared to one or more control samples.

The term “sequence of interest” herein refers to a nucleic acid sequencethat is associated with a difference in sequence representation inhealthy versus diseased individuals. A sequence of interest can be arepeat sequence on a chromosome that is expanded in a disease or geneticcondition. A sequence of interest may be a portion of a chromosome, agene, a coding or non-coding sequence.

The term “Next Generation Sequencing (NGS)” herein refers to sequencingmethods that allow for massively parallel sequencing of clonallyamplified molecules and of single nucleic acid molecules. Non-limitingexamples of NGS include sequencing-by-synthesis using reversible dyeterminators, and sequencing-by-ligation.

The term “parameter” herein refers to a numerical value thatcharacterizes a physical property. Frequently, a parameter numericallycharacterizes a quantitative data set and/or a numerical relationshipbetween quantitative data sets. For example, a ratio (or function of aratio) between the number of sequence tags mapped to a chromosome andthe length of the chromosome to which the tags are mapped, is aparameter.

The term “call criterion” herein refers to any number or quantity thatis used as a cutoff to characterize a sample such as a test samplecontaining a nucleic acid from an organism suspected of having a medicalcondition. The threshold may be compared to a parameter value todetermine whether a sample giving rise to such parameter value suggeststhat the organism has the medical condition. In certain embodiments, athreshold value is calculated using a control data set and serves as alimit of diagnosis of a repeat expansion in an organism. In someimplementations, if a threshold is exceeded by results obtained frommethods disclosed herein, a subject can be diagnosed with a repeatexpansion. Appropriate threshold values for the methods described hereincan be identified by analyzing values calculated for a training set ofsamples or control samples. Threshold values can also be calculated fromempirical parameters such as sequencing depth, read length, repeatsequence length, etc. Alternatively, affected samples known to haverepeat expansion can also be used to confirm that the chosen thresholdsare useful in differentiating affected from unaffected samples in a testset. The choice of a threshold is dependent on the level of confidencethat the user wishes to have to make the classification. In someembodiments, the training set used to identify appropriate thresholdvalues comprises at least 10, at least 20, at least 30, at least 40, atleast 50, at least 60, at least 70, at least 80, at least 90, at least100, at least 200, at least 300, at least 400, at least 500, at least600, at least 700, at least 800, at least 900, at least 1000, at least2000, at least 3000, at least 4000, or more qualified samples. It may beadvantageous to use larger sets of qualified samples to improve thediagnostic utility of the threshold values.

The term “read” refers to a sequence read from a portion of a nucleicacid sample. Typically, though not necessarily, a read represents ashort sequence of contiguous base pairs in the sample. The read may berepresented symbolically by the base pair sequence (in ATCG) of thesample portion. It may be stored in a memory device and processed asappropriate to determine whether it matches a reference sequence ormeets other criteria. A read may be obtained directly from a sequencingapparatus or indirectly from stored sequence information concerning thesample. In some cases, a read is a DNA sequence of sufficient length(e.g., at least about 25 bp) that can be used to identify a largersequence or region, e.g., that can be aligned and mapped to a chromosomeor genomic region or gene.

The term “genomic read” is used in reference to a read of any segmentsin the entire genome of an individual.

The term “site” refers to a unique position (i.e. chromosome ID,chromosome position and orientation) on a reference genome. In someembodiments, a site may be a residue, a sequence tag, or a segment'sposition on a sequence.

As used herein, the term “reference genome” or “reference sequence”refers to any particular known genome sequence, whether partial orcomplete, of any organism or virus which may be used to referenceidentified sequences from a subject. For example, a reference genomeused for human subjects as well as many other organisms is found at theNational Center for Biotechnology Information at ncbi.nlm nih.gov. A“genome” refers to the complete genetic information of an organism orvirus, expressed in nucleic acid sequences.

In various embodiments, the reference sequence is significantly largerthan the reads that are aligned to it. For example, it may be at leastabout 100 times larger, or at least about 1000 times larger, or at leastabout 10,000 times larger, or at least about 10⁵ times larger, or atleast about 10⁶ times larger, or at least about 10⁷ times larger.

In one example, the reference sequence is that of a full length humangenome. Such sequences may be referred to as genomic referencesequences. In another example, the reference sequence is limited to aspecific human chromosome such as chromosome 13. In some embodiments, areference Y chromosome is the Y chromosome sequence from human genomeversion hg19. Such sequences may be referred to as chromosome referencesequences. Other examples of reference sequences include genomes ofother species, as well as chromosomes, sub-chromosomal regions (such asstrands), etc., of any species.

In some embodiments, a reference sequence for alignment may have asequence length from about 1 to about 100 times the length of a read. Insuch embodiments, the alignment and sequencing are considered a targetedalignment or sequencing, instead of a whole genome alignment orsequencing. In these embodiments, the reference sequence typicallyinclude a gene and/or a repeat sequence of interest.

In various embodiments, the reference sequence is a consensus sequenceor other combination derived from multiple individuals. However, incertain applications, the reference sequence may be taken from aparticular individual.

The term “clinically-relevant sequence” herein refers to a nucleic acidsequence that is known or is suspected to be associated or implicatedwith a genetic or disease condition. Determining the absence or presenceof a clinically-relevant sequence can be useful in determining adiagnosis or confirming a diagnosis of a medical condition, or providinga prognosis for the development of a disease.

The term “derived” when used in the context of a nucleic acid or amixture of nucleic acids, herein refers to the means whereby the nucleicacid(s) are obtained from the source from which they originate. Forexample, in one embodiment, a mixture of nucleic acids that is derivedfrom two different genomes means that the nucleic acids, e.g., cfDNA,were naturally released by cells through naturally occurring processessuch as necrosis or apoptosis. In another embodiment, a mixture ofnucleic acids that is derived from two different genomes means that thenucleic acids were extracted from two different types of cells from asubject.

The term “based on” when used in the context of obtaining a specificquantitative value, herein refers to using another quantity as input tocalculate the specific quantitative value as an output.

The term “patient sample” herein refers to a biological sample obtainedfrom a patient, i.e., a recipient of medical attention, care ortreatment. The patient sample can be any of the samples describedherein. In certain embodiments, the patient sample is obtained bynon-invasive procedures, e.g., peripheral blood sample or a stoolsample. The methods described herein need not be limited to humans.Thus, various veterinary applications are contemplated in which case thepatient sample may be a sample from a non-human mammal (e.g., a feline,a porcine, an equine, a bovine, and the like).

The term “biological fluid” herein refers to a liquid taken from abiological source and includes, for example, blood, serum, plasma,sputum, lavage fluid, cerebrospinal fluid, urine, semen, sweat, tears,saliva, and the like. As used herein, the terms “blood,” “plasma” and“serum” expressly encompass fractions or processed portions thereof.Similarly, where a sample is taken from a biopsy, swab, smear, etc., the“sample” expressly encompasses a processed fraction or portion derivedfrom the biopsy, swab, smear, etc.

As used herein, the term “corresponding to” sometimes refers to anucleic acid sequence, e.g., a gene or a chromosome, that is present inthe genome of different subjects, and which does not necessarily havethe same sequence in all genomes, but serves to provide the identityrather than the genetic information of a sequence of interest, e.g., agene or chromosome.

As used herein the term “chromosome” refers to the heredity-bearing genecarrier of a living cell, which is derived from chromatin strandscomprising DNA and protein components (especially histones). Theconventional internationally recognized individual human genomechromosome numbering system is employed herein.

As used herein, the term “polynucleotide length” refers to the absolutenumber of nucleic acid monomer subunits (nucleotides) in a sequence orin a region of a reference genome. The term “chromosome length” refersto the known length of the chromosome given in base pairs, e.g.,provided in the NCB136/hg18 assembly of the human chromosome found at|genome|.|ucsc|.|edu/cgi-bin/hgTracks?hgsid=167155613&chromInfoPage=onthe World Wide Web.

The term “subject” herein refers to a human subject as well as anon-human subject such as a mammal, an invertebrate, a vertebrate, afungus, a yeast, a bacterium, and a virus. Although the examples hereinconcern humans and the language is primarily directed to human concerns,the concepts disclosed herein are applicable to genomes from any plantor animal, and are useful in the fields of veterinary medicine, animalsciences, research laboratories and such.

The term “primer,” as used herein refers to an isolated oligonucleotidethat is capable of acting as a point of initiation of synthesis whenplaced under conditions inductive to synthesis of an extension product(e.g., the conditions include nucleotides, an inducing agent such as DNApolymerase, and a suitable temperature and pH). The primer may bepreferably single stranded for maximum efficiency in amplification, butalternatively may be double stranded. If double stranded, the primer isfirst treated to separate its strands before being used to prepareextension products. The primer may be an oligodeoxyribonucleotide. Theprimer is sufficiently long to prime the synthesis of extension productsin the presence of the inducing agent. The exact lengths of the primerswill depend on many factors, including temperature, source of primer,use of the method, and the parameters used for primer design.

Introduction

Repeat expansions are a special class of microsatellite andminisatellite variants involving STR polymorphisms. Repeat expansionsare also known as dynamic mutations due to their instability when shorttandem repeats expand beyond certain sizes. Genetic disorders caused byunstable repeat expansions include fragile X syndrome, Huntington'sdisease, and ALS. Table 1 exemplifies a small number of pathogenicrepeat expansions that are different from repeat sequences in normalsamples. The columns show genes associated with the repeat sequences,the nucleic acid sequences of the repeat units, the numbers of repeatsof the repeat units for normal and pathogenic sequences, and thediseases associated with the repeat expansions.

TABLE 1 Examples of pathogenic repeat expansions Gene Repeat NormalPathogenic Disease FMR1 CGG 6-60 200-900  Fragile X AR CAG 9-36 38-62 Spino-bulbar muscular atrophy GHTT CAG 11-34  40-121 Huntington'sdisease FXN GAA 6-32 200-1700 Fredreich's ataxia ATXN1 CAG 6-39 40-82 Spinocerebellar ataxia ATXN10 ATTCT 10-20  500-4500 Spinocerebellarataxia ATXN2 CAG 15-24  32-200 Spinocerebellar ataxia ATXN3 CAG 13-36 61-84  Spinocerebellar ataxia ATXN7 CAG 4-35 37-306 Spinocerebellarataxia C9ofr72 GGGGCC <30 100's ALS

Genetic disorders involving repeat expansions are heterogeneous in manyrespects. The size of the repeat unit, degree of expansion, locationwith respect to the affected gene, and pathogenic mechanism may varyfrom disorder to disorder. For example, ALS involves a hexanucleotiderepeat expansion of the nucleotides GGGGCC in the C9orf72 gene locatedon the short arm of chromosome 9 open reading frame 72. In contrast,Fragile X syndrome is associated with the expansion of the CGGtrinucleotide repeat (triplet repeat) affecting the Fragile X mentalretardation 1 (FMR]) gene on the X chromosome. An expansion of the CGGrepeats can result in a failure to express the fragile X mentalretardation protein (FMRP), which is required for normal neuraldevelopment. Depending on the length of the CGG repeat, an allele may beclassified as normal (unaffected by the syndrome), a pre-mutation (atrisk of fragile X associated disorders), or full mutation (usuallyaffected by the syndrome). According to various estimates, there arefrom 230 to 4000 CGG repeats in mutated FMR1 genes that cause fragile Xsyndrome in affected patients, as compared with 60 to 230 repeats incarriers prone to ataxia, and 5 to 54 repeats in unaffected individuals.Repeat expansion of the FMR1 gene is a cause for autism, as about 5% ofautistic individuals are found to have the FMR1 repeat expansion.McLennan, et al. (2011), Fragile X Syndrome, Current Genomics 12 (3):216-224. A definitive diagnosis of fragile X syndrome involves genetictesting to determine the number of CGG repeats.

Various general properties of repeat expansion related diseases havebeen identified in multiple studies. Repeat expansion or dynamicmutation is usually manifested as an increase in repeat number, withmutation rate being related to the number of repeats. Rare events suchas loss of repeat interruption can lead to alleles having an increasedlikelihood of expanding, with such events being known as founder events.There may be a relationship between the number of repeats in the repeatsequence and the severity and/or onset of the disease caused by repeatexpansion.

Therefore, identifying and calling repeat expansions is important in thediagnosis and treatment of various diseases. However, identifying repeatsequences, especially using reads that do not fully traverse the repeatsequence, has various challenges. First, it is difficult to alignrepeats to a reference sequence because there is not a clear one-to-onemapping between the read and the reference genome. Additionally, even ifa read is aligned to a reference sequence, the reads are often too shortto fully cover a medically relevant repeat sequence. For instance, thereads may be about 100 bp. In comparison, a repeat expansion can spanhundreds to thousands of base pairs. In fragile X syndrome, for example,the FMR1 gene can have well over 1000 repeats, spanning over 3000 bp. Soa 100-bp read cannot map the full length of the repeat expansion.Moreover, assembling short reads into a longer sequence may not overcomethe short read versus long repeat problem, because it is difficult toassemble short reads into a longer sequence due to the ambiguousalignment of repeats in one read with repeats on another read.

Alignment is the primary culprit for loss of information either due toincompleteness of the reference sequence, non-unique correspondencebetween a read and sites on the reference sequence, or significantdeviations from the reference sequence. Systematic sequencing errors andother issues affecting read accuracy are a secondary factor for failurein detecting repeat sequences. In some experimental protocols, about 7%reads are unaligned or with a MAPQ score of 0. Even as researchers workto improve sequencing technology and analysis tools, there will alwaysbe a significant amount of unalignable and poorly aligned reads.Implementations of the methods herein rely on unalignable or poorlyaligned reads to identify repeat expansions.

Methods using long reads to detect repeat expansion have their ownchallenges. In next generation sequencing, currently availabletechnologies using longer reads are slower and more error prone thantechnologies using shorter reads. Moreover, long reads are not feasiblefor some applications, such as sequencing cell-free DNA. Cell-free DNAobtained in maternal blood can be used for pre-natal genetic diagnosis.The cell-free DNA exists as fragments typically shorter than 200 bp. Assuch, methods using long reads are not feasible for pre-natal geneticdiagnosis using cell-free DNA. Implementations of the methods describedherein use short reads to identify repeat expansions that are medicallyrelevant.

In some implementations, the disclosed methods address aforementionedchallenges in identifying and calling repeat expansions by utilizingpaired end sequencing. Paired end sequencing involves fragmenting DNAinto sequences called inserts. In some protocols such as some used byIllumina, the reads from shorter inserts (e.g. on the order of tens tohundreds of bp) are referred to as short-insert paired end reads orsimply paired end reads. In contrast, the reads from longer inserts(e.g., on the order of several thousands of bp) are referred to as matepair reads. As noted above, short-insert paired end reads andlong-insert mate pair reads may both be used in various implementationsof the methods disclosed herein.

FIG. 1A is a schematic illustration showing certain difficulties inaligning sequence reads to a repeat sequence on a reference sequence,especially when aligning sequence reads obtained from a sample of a longrepeat sequence having a repeat expansion. At the bottom of FIG. 1A is areference sequence 101 having a relatively short repeat sequence 103illustrated by vertical hatch lines. In the middle of figure is ahypothetical sequence 105 of a patient sample having a long repeatsequence 107 harboring a repeat expansion, also illustrated by verticalhatch lines. Illustrated at the top of the figure are sequence reads 109and 111 shown at locations of corresponding sites of the sample sequence105. In some of these sequence reads, e.g., reads 111, some base pairsoriginate from the long repeat sequence 107, as illustrated also byvertical hatch lines and highlighted in a circle. Reads 111 having theserepeats are potentially difficult to align to the reference sequence101, because the repeats do not have clear corresponding locations onthe reference sequence 101. Because these potentially unaligned readscannot be clearly associated with the repeat sequence 103 in thereference sequence 101, it is difficult to obtain information regardingthe repeat sequence and the expansion of the repeat sequence from thesepotentially unaligned reads 111. Furthermore, because these reads tendto be shorter than the long repeat sequence 107 harboring the repeatexpansion, they cannot directly provide definitive information about theidentity or location of the repeat sequence 107. Additionally, therepeats in the reads 111 make them difficult to assemble due to theirambiguous corresponding locations on the reference sequence 101 and theambiguous relation amongst the reads 111. The reads that come partlyfrom the long repeat sequence 107 in the sample, those illustrated ashalf hatched and half solid-black, may be aligned by the basesoriginating from outside of the repeat sequence 107. If the reads havetoo few base pairs outside of the repeat sequence 107, the reads may bepoorly aligned or may not be aligned. So some of these reads withpartial repeats may be analyzed as anchor reads, and others analyzed asanchored reads as further described below.

FIG. 1B is a schematic diagram illustrating how paired end reads may beutilized in some disclosed embodiments to overcome the difficultiesshown in FIG. 1A. In paired end sequencing, sequencing occurs from bothends of fragments of nucleic acids in a test sample. Illustrated at thebottom of FIG. 1B are a reference sequence 101 and a sample sequence105, as well as reads 109 and 111 equivalent to those shown in FIG. 1A.Illustrated at the top of FIG. 1B is a fragment 125 derived from a testsample sequence 105 and a read 1 primer region 131 and a read 2 primerregion 133 for obtaining two reads 135 and 137 of the paired end reads.The fragment 125 is also referred to as an insert for the paired endreads. In some embodiments, inserts may be amplified with or withoutPCR. Some repeat sequences, such as those including a large number of GCor GCC repeats, cannot be sequenced well with traditional methods thatinclude PCR amplification. For such sequences, amplification might bePCR-free. For other sequences, amplification may be performed with PCR.

The insert 125 illustrated in FIG. 1B corresponds to, or is derivedfrom, a section of the sample sequence 105 flanked by two verticalarrows illustrated at the lower half of the figure. Specifically, theinsert 125 harbors a repeat section 127 corresponding to part of thelong repeat 107 in the sample sequence 105. The length of inserts may beadjusted for various applications. In some embodiments, the inserts maybe somewhat shorter than the repeat sequence of interest or the repeatsequence having the repeat expansion. In other embodiments, the insertsmay have a similar length to the repeat sequence or the repeat sequencehaving a repeat expansion. In yet further embodiments, the inserts mayeven be somewhat longer than the repeat sequence or the repeat sequencehaving the repeat expansion. Such inserts may be long inserts for matepair sequencing in some embodiments further described below. Typically,the reads obtained from the inserts are shorter than the repeatsequence. Because inserts are longer than reads, paired end reads canbetter capture signals from a longer stretch of repeat sequence in thesample than single end reads.

The illustrated insert 125 has two read primer regions 131 and 133 attwo ends of the insert. In some embodiments, read primer regions areinherent to the insert. In other embodiments, the primer regions areintroduced to the insert by ligation or extension. Illustrated on theleft end of the insert is a read 1 primer region 131, which allows thehybridization of a read 1 primer 132 to the insert 125. The extension ofthe read 1 primer 132 generates a first read, or read 1, labeled as 135Illustrated on the right end of the insert 125 is a read 2 primer region133, which allows the hybridization of a read 2 primer 134 to the insert125, initiating the second read, or read 2, labeled as 137. In someembodiments, the insert 125 may also include index barcode regions (notshown in the figure here), providing a mechanism to identify differentsamples in a multiplex sequencing process. In some embodiments, thepaired end reads 135 and 137 may be obtained by Illumina's sequencing bysynthesis platforms. An example of a sequencing process implemented onsuch a platform is further described hereinafter in the SequencingMethods Section, which process creates two paired end reads and twoindex reads.

The paired end reads obtained as illustrated in FIG. 1B may then bealigned to the reference sequence 101 having a relatively short repeatsequence 103. As such, the relative location and direction of a pair ofreads are known. This allows an unalignable or poorly aligned read suchas those shown in circle 111 to be indirectly associated with therelatively long repeat sequence 107 in the sample sequence 105 throughthe read's corresponding paired read 109 as seen at the bottom of FIG.1B. In an illustrative example, the reads obtained from paired endsequencing are about 100 bp and the inserts are about 500 bp. In thisexample setup, the relative locations of the two paired end reads areabout 300 base pairs apart from their 3′ ends, and they have oppositedirections. The relationship between the read pairs allows one to betterassociate reads to repeat regions. In some cases, a first read in a pairaligns with a non-repeat sequence flanking the repeat region on areference sequence, and the second read in the pair does not properlyalign to the reference. See, for example, the pair of reads 109 a and111 a illustrated in the bottom half of FIG. 1B, with the left one 109 aof the pair being the first read, and the right one 111 a being thesecond read. Given the pairing of the two reads 109 a and 111 a, thesecond read 111 a can be associated with the repeat region 107 in thesample sequence 105, despite the fact that the second read 111 a cannotbe aligned to the reference sequence 101. Knowing the distance anddirection of the second read 111 a relative to the first read 109 a, onecan further determine the location of the second read 111 a within thelong repeat region 107. If a break exists among repeats in the secondread 111 a, the break's location relative to the reference sequence 101may also be determined. A read such as the left read 109 a that isaligned to the reference is referred to as an anchor read in thisdisclosure. A read such as the right one 111 a that is not aligned tothe reference sequence but is paired with an anchor read is referred toas an anchored read. As such, an unaligned sequence can be anchored toand associated with the repeat expansion. In this manner one can useshort reads to detect long repeat expansions. While the challenge ofdetecting repeat expansions typically increases with the length of theexpansion due to increased difficulty of sequencing, the methodsdisclosed herein can detect a higher signal from longer repeat expansionsequences than from shorter repeat expansion sequences. This is sobecause as the repeat sequence or repeat expansion gets longer, morereads will be anchored to the expansion region, more reads can fallcompletely in the repeat region, and more repeats can occur per read.

In some embodiments, the disclosed methods involve analyzing thefrequency distribution of the numbers of repeats found in the anchor andanchored reads. In some embodiments, only the anchored reads areanalyzed. In other embodiments, both the anchor and anchored reads areanalyzed. The distribution of a test sample may be compared to anempirically or theoretically derived criterion separating unaffectedsamples from affected samples. In this way one may determine whether ornot the test sample has the repeat expansion under consideration, andmake a clinically relevant call.

The methods and apparatus described herein may employ next generationsequencing technology (NGS), which allows massively parallel sequencing.In certain embodiments, clonally amplified DNA templates or single DNAmolecules are sequenced in a massively parallel fashion within a flowcell (e.g., as described in Volkerding et al. Clin Chem 55:641-658[2009]; Metzker M Nature Rev 11:31-46 [2010]). The sequencingtechnologies of NGS include but are not limited to pyrosequencing,sequencing-by-synthesis with reversible dye terminators, sequencing byoligonucleotide probe ligation, and ion semiconductor sequencing. DNAfrom individual samples can be sequenced individually (i.e., singleplexsequencing) or DNA from multiple samples can be pooled and sequenced asindexed genomic molecules (i.e., multiplex sequencing) on a singlesequencing run, to generate up to several hundred million reads of DNAsequences. Examples of sequencing technologies that can be used toobtain the sequence information according to the present method arefurther described below.

Various repeat expansion analyses using DNA samples involve aligning ormapping sequence reads from a sequencer to a reference sequence. Areference sequence may be the sequence of a whole genome, the sequenceof a chromosome, the sequence of a sub-chromosomal region, etc. From acomputational perspective, repeats create ambiguities in alignment,which, in turn, can produce biases and errors even at the wholechromosome counting level. Paired end reads coupled with adjustableinsert length in various embodiments can help to eliminate ambiguity inalignment of repeating sequences and detection of repeat expansion.

Identifying Repeat Expansions

Using the embodiments disclosed herein, one can determine variousgenetic conditions related to repeat expansion with high efficiency,sensitivity, and/or selectivity relative to conventional methods. Someembodiments of the invention provide methods for identifying and callingmedically relevant repeat expansions such as the CGG repeat expansionthat causes mental retardation in Fragile X syndrome using sequencereads that do not fully traverse the repeat sequence. Short reads suchas 100 bp reads are not long enough to sequence through many repeatexpansions. However, when analyzed with disclosed methods, samples witha repeat expansion show a statistically significant excess of readscontaining a large number of the repeat sequence. Additionally,extremely large repeat expansions contain unaligned read pairs whereboth reads are entirely or almost entirely composed of the repeatsequence. Normal samples are used to identify the backgroundexpectations.

Conventional belief is that a repeat expansion cannot be detectedwithout reads that span the entire repeat. Prior approaches to detectingrepeat expansions use targeted sequencing with long reads and in somecases have been unsuccessful due to reads that are not long enough tospan the repeat sequence. The results of some disclosed embodiments havebeen met with surprise partly because they use normal (non-targeted)sequence data and read length of only about 100 bp, but result in veryhigh sensitivity for detecting repeat expansions. The methods set forthherein can detect the number of repeat units in a repeat expansion usingpaired reads having an insert length (i.e. two sequence reads andintervening sequence) that is shorter than the length of the entirerepeat sequence.

Turning to the details of methods for determining the presence of repeatexpansion according to some embodiments, FIG. 2 shows a flow diagramproviding a high level depiction of embodiments for determining thepresence or absence of a repeat expansion of a repeat sequence in asample. The repeat sequence is a nucleic acid sequence including therepetitive appearance of a short sequence referred to as a repeat unit.Table 1 above provides examples of repeat units, the numbers of repeatsof the repeat units in the repeat sequences for normal and pathogenicsequences, the genes associated with the repeat sequences, and thediseases associated with the repeat expansion. Process 200 in FIG. 2starts by obtaining paired end reads of a test sample. See block 202.The paired end reads have been processed to align to a referencesequence including a repeat sequence of interest. In some contexts, thealignment process is also referred to as a mapping process. The testsample includes nucleic acid and may be in the form of bodily fluids,tissues, etc., such as further described in the Sample Section below.The sequence reads have undergone an alignment process to be mapped to areference sequence. Various alignment tools and algorithms may be usedto attempt to align reads to the reference sequence as describedelsewhere in the disclosure. As usual, in alignment algorithms, somereads are successfully aligned to the reference sequence, while othersmay not be successfully aligned or may be poorly aligned to thereference sequence. Reads that are successively aligned to the referencesequence are associated with sites on the reference sequence. Alignedreads and their associated sites are also referred to as sequence tags.As explained above, some sequence reads that contain a large number ofrepeats tend to be harder to align to the reference sequence. When aread is aligned to a reference sequence with a number of mismatchedbases above a certain criterion, the read is considered poorly aligned.In various embodiments, reads are considered poorly aligned when theyare aligned with at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10mismatches. In other embodiments, reads are considered poorly alignedwhen they are aligned with at least about 5% of mismatches. In otherembodiments, reads are considered poorly aligned when is they arealigned with at least about 10%, 15%, or 20% mismatched bases.

As illustrated in FIG. 2, process 200 proceeds to identify anchor readsand anchored reads in the paired end reads. See block 204. Anchor readsare reads among the paired end reads that are aligned to or near therepeat sequence of interest. For instance, an anchor read can align to alocation on a reference sequence that is separated from a repeatsequence by a sequence length that is less than the sequence length ofthe insert. The separation length can be shorter. For example, theanchor read can align to a location on a reference sequence that isseparated from a repeat sequence by a sequence length that is less thanthe sequence length of the anchor read or less than the combinedsequence length of the anchor read and the sequence that connects theanchor read to the anchored read (i.e. the length of the insert minusthe length of the anchored read). In some embodiments, the repeatsequence of interest may be the repeat sequence in the FMR1 geneincluding repeats of the repeat unit CGG. In a normal referencesequence, the repeat sequence in FMR1 gene includes about 6-32 repeatsof the repeat unit CGG. As the repeats expand to over 200 copies, therepeat expansion tends to become pathogenic, causing Fragile X syndrome.In some embodiments, reads are considered aligned near the sequence ofinterest when it is aligned within 1000 bp of the repeat sequence ofinterest. In other embodiments, this parameter may be adjusted, such aswithin about 100 bp, 200 bp, 300 bp, 400 bp, 500 bp, 600 bp, 700 bp, 800bp, 900 bp, 1500 bp, 2000 bp, 3000 bp, 5000 bp, etc. Additionally, theprocess also identifies anchored reads, which are reads that are pairedto anchor reads, but are poorly aligned to or cannot be aligned to theirreference sequence. Additional details of poorly aligned reads aredescribed above.

Process 200 further involves determining if the repeat expansion of therepeat sequence is likely to be present in the test sample based atleast in part on the identified anchored reads. See block 206. Thisdetermination step can involve various suitable analyses andcomputations as further described below. In some embodiments, theprocess uses the identified anchor reads, as well as the anchored reads,to determine if the repeat expansion is likely to be present. In someembodiments, the numbers of the repeats in the identified anchor andanchored reads are analyzed and compared to one or more criteria derivedtheoretically or derived from empirical data of an affected controlsamples.

In various embodiments described herein, repeats are obtained asin-frame repeats, where two repeats of the same repeat unit fall in thesame reading frame. A reading frame is a way of dividing the sequence ofnucleotides in a nucleic acid (DNA or RNA) molecule into a set ofconsecutive, non-overlapping triplets. During translation, tripletsencode amino acids, and are termed codons. So any particular sequencehas three possible reading frames. In some embodiments, repeats arecounted according to three different reading frames, and the largest ofthe three counts is determined to be the number of corresponding repeatsfor the read.

An example of a process involving additional operation and analyses isillustrated in FIG. 3. FIG. 3 shows a flow diagram illustrating aprocess 300 for detecting repeat expansion using paired end reads havinga large number of repeats. Process 300 includes additional upstream actsfor processing the test sample. The process starts by sequencing a testsample including nucleic acids to obtain paired end reads. See block302. In some embodiments, the test sample may be obtained and preparedin various ways as further described in the Samples Section below. Forinstance, the test sample may be a biological fluid, e.g., plasma, orany suitable sample as described below. The sample may be obtained usinga non-invasive procedure such as a simple blood draw. In someembodiments, a test sample contains a mixture of nucleic acid molecules,e.g., cfDNA molecules. In some embodiments, the test sample is amaternal plasma sample that contains a mixture of fetal and maternalcfDNA molecules.

Before sequencing, nucleic acids are extracted from the sample. Suitableextraction processes and apparatus are described elsewhere herein. Insome implementations, the apparatus processes DNA from multiple samplestogether to provide multiplexed libraries and sequence data. In someembodiments, the apparatus processes DNA from eight or more test samplesin parallel. As described below, a sequencing system may processextracted DNA to produce a library of coded (e.g., bar coded) DNAfragments.

In some embodiments, the nucleic acids in the test sample may be furtherprocessed to prepare sequencing libraries for multiplex or singleplexsequencing, as further described in the Sequencing Library PreparationSection below. After the samples are processed and prepared, sequencingof the nucleic acid may be performed by various methods. In someembodiments, various next generation sequencing platforms and protocolsmay be employed, which are further described in the Sequencing MethodsSection below.

Regardless of the specific sequencing platform and protocol, in block302, at least a portion of the nucleic acids contained in the sample aresequenced to generate tens of thousands, hundreds of thousands, ormillions of sequence reads, e.g., 100 bp reads. In some embodiments, thereads include paired end reads. In other embodiments, such as thosedescribed below with reference to FIG. 5, in addition to paired endreads, single-end long reads including over hundreds, thousands, or tensof thousands bases may be used to determine a repeat sequence. In someembodiments, the sequence reads comprise about 20 bp, about 25 bp, about30 bp, about 35 bp, about 36 bp, about 40 bp, about 45 bp, about 50 bp,about 55 bp, about 60 bp, about 65 bp, about 70 bp, about 75 bp, about80 bp, about 85 bp, about 90 bp, about 95 bp, about 100 bp, about 110bp, about 120 bp, about 130, about 140 bp, about 150 bp, about 200 bp,about 250 bp, about 300 bp, about 350 bp, about 400 bp, about 450 bp, orabout 500 bp. It is expected that technological advances will enablesingle-end reads of greater than 500 bp and enabling for reads ofgreater than about 1000 bp when paired end reads are generated.

Process 300 proceeds to align the paired end reads obtained from block302 to a reference sequence including a repeat sequence. See block 304.In some embodiments, the repeat sequence is prone to expansion. In someembodiments the repeat expansion is known to be associated with agenetic disorder. In other embodiments, the repeat expansion of therepeat sequence has not been a previously studied to establish anassociation with a genetic disorder. The methods disclosed herein allowdetection of a repeat sequence and repeat expansion regardless of anyassociated pathology. In some embodiments, reads are aligned to areference genome, e.g., hg18. In other embodiments, reads are aligned toa portion of a reference genome, e.g., a chromosome or a chromosomesegment. The reads that are uniquely mapped to the reference genome areknown as sequence tags. In one embodiment, at least about 3×10⁶qualified sequence tags, at least about 5×10⁶ qualified sequence tags,at least about 8×10⁶ qualified sequence tags, at least about 10×10⁶qualified sequence tags, at least about 15×10⁶ qualified sequence tags,at least about 20×10⁶ qualified sequence tags, at least about 30×10⁶qualified sequence tags, at least about 40×10⁶ qualified sequence tags,or at least about 50×10⁶ qualified sequence tags are obtained from readsthat map uniquely to a reference genome.

In some embodiments, the process may filter sequence reads prior toalignment. In some embodiments, read filtering is a quality-filteringprocess enabled by software programs implemented in the sequencer tofilter out erroneous and low quality reads. For example, Illumina' sSequencing Control Software (SCS) and Consensus Assessment of Sequenceand Variation software programs filter out erroneous and low qualityreads by converting raw image data generated by the sequencing reactionsinto intensity scores, base calls, quality scored alignments, andadditional formats to provide biologically relevant information fordownstream analysis.

In certain embodiments, the reads produced by sequencing apparatus areprovided in an electronic format. Alignment is accomplished usingcomputational apparatus as discussed below. Individual reads arecompared against the reference genome, which is often vast (millions ofbase pairs) to identify sites where the reads uniquely correspond withthe reference genome. In some embodiments, the alignment procedurepermits limited mismatch between reads and the reference genome. In somecases, 1, 2, 3, or more base pairs in a read are permitted to mismatchcorresponding base pairs in a reference genome, and yet a mapping isstill made. In some embodiments, reads are considered aligned reads whenthe reads are aligned to the reference sequence with no more than 1, 2,3, or 4 base pairs. Correspondingly, unaligned reads are reads thatcannot be aligned or are poorly aligned. Poorly aligned reads are readshaving more mismatches than aligned reads. In some embodiments, readsare considered aligned reads when the reads are aligned to the referencesequence with no more than 1%, 2%, 3%, 4%, 5%, or 10% of base pairs.

After aligning the paired end reads to the reference sequence includingthe repeat sequence of interest, process 300 proceeds to identify anchorreads and anchored reads among the paired end reads. See block 306. Asmentioned above, anchor reads are paired end reads aligned to or nearthe repeat sequence. In some embodiments anchor reads are paired endreads that are aligned within 1 kb of the repeat sequence. Anchoredreads are paired with anchor reads, but they cannot be or are poorlyaligned to the reference sequence as explained above.

Process 300 analyzes the numbers of repeats of repeat units in theidentified anchor and/or anchored reads to determine the presence orabsence of an expansion of the repeat sequence. More specifically,process 300 involves using the numbers of repeats in reads to obtainnumbers of high-count reads in anchor and/or anchored reads. High-countreads are reads having more repeats than a threshold value. In someembodiments, the high-count reads are obtained only from the anchoredreads. In other embodiments, the high-count reads are obtained from boththe anchor and anchored reads. In some embodiments, if the number ofrepeats is close to the maximum number of repeats possible for a read,the read is considered a high-count read. For instance, if a read is 100bp, and a repeat unit under consideration is 3 bp, the maximum number ofrepeats would be 33. In other words, the maximum is calculated from thelength of the paired end reads and the length of the repeat unit.Specifically, the maximum number of repeats may be obtained by dividingthe read length by the length of the repeat unit and rounding down thenumber. In this example, various implementations may identify 100 bpreads having at least about 28, 29, 30, 31, 32, or 33 repeats ashigh-count reads. The number of repeats may be adjusted upward ordownward for high-count reads based on empirical factors andconsiderations. In various embodiments, the threshold value forhigh-count reads is at least about 80%, 85%, 90%, or 95% of the maximumnumber of repeats.

Process 300 then determines if a repeat expansion of the repeat sequenceis likely present based on the number of high-count reads. See block310. In some embodiments, the analysis compares the obtained high-countreads to a call criterion, and determines that the repeat expansion islikely present if the criterion is exceeded. In some embodiments, thecall criterion is obtained from a distribution of high-count reads ofcontrol samples. For instance, a plurality of control samples known tohave or suspected of having a normal repeat sequence are analyzed, andhigh-count reads are obtained for the control samples in the same way asdescribed above. The distribution of high count reads for the controlsamples can be obtained, and the probability of an unaffected samplehaving high count reads more than a particular value can be estimated.This probability allows determination of sensitivity and selectivitygiven a call criterion set at this particular value. In someembodiments, the call criterion is set at a threshold value such thatthe probability of an unaffected sample having high-count reads morethan the threshold value is less than 5%. In other words, the p-value issmaller than 0.05. In these embodiments, as the repeats expand, therepeat sequence gets longer, more reads are possible to originate fromcompletely within the repeat sequence, and more high-count reads can beobtained for a sample. In various alternative implementations, a moreconservative call criterion may be chosen such that the probability ofan unaffected sample having more high-count reads than the thresholdvalue is less than about 1%, 0.1%, 0.01%, 0.001%, 0.0001%, etc. It willbe appreciated that the call criterion can be adjusted upward ordownward based on the various factors and the need to increasesensitivity or selectivity of the test.

In some embodiments, instead of or in addition to empirically obtaininga call criterion of the number of high-count reads from control samples,a call criterion may be obtained theoretically for determining a repeatexpansion. It is possible to calculate the expected number of reads thatare fully within a repeat given a number of parameters including thelength of the paired end reads, the length of a sequence having therepeat expansion, and a sequencing depth. For instance, one can use asequencing depth to calculate the average spacing between reads in thealigned genome. If one has sequenced an individual sample to 30× depth,the total bases sequenced are equal to the size of the genome multipliedby the depth. For humans this would amount to about 3×10^(9×30=9×)10¹⁰.If each read is 100 bp long, then there are a total of 9×10⁸ readsrequired to achieve this depth. Since a genome is diploid, half of thesereads are sequencing one chromosome/haplotype, and the rest aresequencing the other chromosome/haplotype. Per haplotype there are4.5×10⁸ reads and dividing the total genome size by this number yieldsthe average spacing between starting positions of each read—i.e.3×10⁹/4.5×10⁸=1 read every 6.7 bp on average. One can use this number toestimate the number of reads that will be fully within a repeat sequencebased on the size of that repeat sequence in a particular individual. Ifthe total repeat sequence size is 300 bp then any read that startswithin the first 200 bp of that repeat sequence will be fully within therepeat sequence (any read that starts within the last 100 bp will be, atleast, partially outside of the repeat sequence based on 100 bp readlengths). Since it is expected that a read will align every 6.7 bp, oneexpects 200 bp/(6.7 bp/read)=30 reads to fully align within the repeatsequence. Though there will be variability around this number, thisallows one to estimate the total reads that will be fully within therepeat sequence for any expansion size. Repeat sequence lengths andcorresponding expected numbers of reads fully aligned in the repeatsequence calculated according to this method are given in Table 2 ofExample 1 below.

In some embodiments, a call criterion is calculated from the distancebetween the first and last observation of the repeat sequence within thereads, thus allowing for mutations in the repeat sequence and sequencingerrors.

In some embodiments, the process may further include diagnosing that theindividual from whom the test sample is obtained with an elevated riskof a genetic disorder such as Fragile X syndrome, ALS, Huntington'sdisease, Friedreich's ataxia, spinocerebellar ataxia, spino-bulbarmuscular atrophy, myotonic dystrophy, Machado-Joseph disease,dentatorubral pallidoluysian atrophy, etc. Such a diagnosis may be basedon a determination that the repeat expansion is likely present in thetest sample, and on the gene and repeat sequence associated with therepeat expansion. In other embodiments, when a genetic disorder is notknown, some embodiments may detect abnormally high counts of repeats tonewly identify genetic causes of a disease.

FIG. 4 is a flowchart illustrating another process for detecting repeatexpansion according to some embodiments. Process 400 uses the numbers ofrepeats in the paired end reads of the test sample instead of high-countreads to determine the presence of the repeat expansion. Process 400starts by sequencing a test sample including nucleic acid to obtainpaired end reads. See block 402, which is equivalent to block 302 ofprocess 300. Process 400 continues by aligning the paired end reads to areference sequence including the repeat sequence. See block 404, whichis equivalent to block 304 in process 300. The process proceeds byidentifying anchor and anchor reads in the paired end reads, with anchorreads being reads aligned to or near the repeat sequence, and theanchored reads being unaligned reads that are paired with the anchorreads. In some embodiments, unaligned reads include both reads thatcannot be aligned to and reads that are poorly aligned to the referencesequence.

After identifying the anchor and anchored reads, process 400 obtains thenumbers of repeats in the anchor and/or anchored reads from the testsample. See block 408. The process then obtains a distribution of thenumbers of repeats for all the anchor and/or anchored reads obtainedfrom the test sample. In some embodiments, only the numbers of repeatsfrom anchored reads are analyzed. In other embodiments, repeats of bothanchored reads and anchor reads are analyzed. Then the distribution ofthe numbers of repeats of the test sample is compared to a distributionof one or more control samples. See block 410. In some embodiments, theprocess determines that repeat expansion of the repeat sequence ispresent in the test sample if the distribution of the test samplestatistically significantly differs from the distribution of the controlsamples. See the block 412. Process 400 analyzes numbers of repeats forreads including high-count as well as low-count reads, which isdifferent from a process that analyzes only high-count reads, such asdescribed above with respect to process 300.

In some embodiments, comparison of the test sample's distribution andthe control samples' distribution involves using a Mann-Whitney ranktest to determine if the two distributions are significantly different.In some embodiments, the analysis determines that the repeat expansionis likely present in the test sample if the test sample's distributionis skewed more towards higher numbers of repeats relative to the controlsamples, and the p-value for the Mann-Whitney rank test is smaller thanabout 0.0001 or 0.00001. The p-value may be adjusted as necessary toimprove selectivity or sensitivity of the test.

The processes for detecting repeat expansion described above withrespect to FIGS. 2-4 use anchored reads, which are unaligned reads thatare paired to reads aligned to a repeat sequence of interest. Variationson these processes could include searching through the unaligned readsfor read pairs that are both almost entirely composed of some type ofrepeat sequence to discover new, previously unidentified repeatexpansions that may be medically relevant. This method does not quantifythe exact number of repeats but is powerful to identify extreme repeatexpansions or outliers that should be flagged for furtherquantification. Combined with longer reads this method may be able toboth identify and quantify repeats of up to 200 bp or more in totallength.

FIG. 5 illustrates a flow diagram of a process 500 that uses unalignedreads not associated with any repeat sequence of interest to identify arepeat expansion. Process 500 may use whole genome unaligned reads todetect repeat expansion. The process starts by sequencing a test sampleincluding nucleic acids to obtain paired end reads. See block 502.Process 500 proceeds by aligning the paired end reads to a referencegenome. See block 504. The process then identifies unaligned reads forthe whole genome. The unaligned reads include paired end reads thatcannot be aligned or are poorly aligned to the reference sequence. Seeblock 506. The process then analyzes the numbers of repeats of a repeatunit in the unaligned reads to determine if a repeat expansion is likelypresent in the test sample. This analysis can be agnostic of anyparticular repeat sequence. The analysis can be applied to variouspotential repeat units, and the numbers of repeats for different repeatunits from a test sample can be compared to those of a plurality ofcontrol samples. Comparison techniques between a test sample and controlsamples described above may be applied in this analysis. If thecomparison shows that a test sample has an abnormally high number ofrepeats of a repeat unit, an additional analysis may be performed todetermine if the test sample includes the repeat expansion of theparticular repeat sequence of interest. See block 510.

In some embodiments, the additional analysis involves very long sequencereads that potentially can span long repeat sequences having repeatexpansions that are medically relevant. The reads in this additionalanalysis are longer than the paired end reads. In some embodiments,single molecule sequencing or synthetic long-read sequencing are used toobtain long reads. In some embodiments, the relation between the repeatexpansion and a genetic disorder is known in the art. In otherembodiments, however, the relation between the repeat expansion and agenetic disorder does not need to be established in the art.

In some embodiments, analyzing the numbers of repeats of the repeat unitin the unaligned reads of operation 510 involves a high-count analysiscomparable to that of operation 308 of FIG. 3. The analysis includesobtaining the number of high-count reads, wherein the high-count readsare unaligned reads having more repeats than a threshold value; andcomparing the number of high-count reads in the test sample to a callcriterion. In some embodiments, the threshold value for high-count readsis at least about 80% of the maximum number of repeats, which maximum iscalculated as the ratio of the length of the paired end reads over thelength of the repeat unit. In some embodiments, the high-count readsalso include reads that are paired to the unaligned reads and have morerepeats than the threshold value.

In some embodiments, prior to the additional analysis of operation 510,the process further involves (a) identifying paired end reads that arepaired to the unaligned reads and are aligned to or near a repeatsequence on the reference genome; and (b) providing the repeat sequenceas the particular repeat sequence of interest for operation 510. Thenthe additional analysis of the repeat sequence of interest may employany of the methods described above in association with FIGS. 2-4.

Samples

Samples that are used for determining repeat expansion can includesamples taken from any cell, fluid, tissue, or organ including nucleicacids in which repeat expansion for one or more repeat sequences ofinterest are to be determined. In some embodiments involving diagnosisof fetus, it is advantageous to obtain cell-free nucleic acids, e.g.,cell-free DNA (cfDNA), from maternal body fluid. Cell-free nucleicacids, including cell-free DNA, can be obtained by various methods knownin the art from biological samples including but not limited to plasma,serum, and urine (see, e.g., Fan et al., Proc Natl Acad Sci105:16266-16271 [2008]; Koide et al., Prenatal Diagnosis 25:604-607[2005]; Chen et al., Nature Med. 2: 1033-1035 [1996]; Lo et al., Lancet350: 485-487 [1997]; Botezatu et al., Clin Chem. 46: 1078-1084, 2000;and Su et al., J Mol. Diagn. 6: 101-107 [2004]).

In various embodiments the nucleic acids (e.g., DNA or RNA) present inthe sample can be enriched specifically or non-specifically prior to use(e.g., prior to preparing a sequencing library). DNA are used as anexample of nucleic acids in the illustrative examples below.Non-specific enrichment of sample DNA refers to the whole genomeamplification of the genomic DNA fragments of the sample that can beused to increase the level of the sample DNA prior to preparing a cfDNAsequencing library. Methods for whole genome amplification are known inthe art. Degenerate oligonucleotide-primed PCR (DOP), primer extensionPCR technique (PEP) and multiple displacement amplification (MDA) areexamples of whole genome amplification methods. In some embodiments, thesample is un-enriched for DNA.

The sample including the nucleic acids to which the methods describedherein are applied typically include a biological sample (“test sample”)as described above. In some embodiments, the nucleic acids to bescreened for repeat expansion are purified or isolated by any of anumber of well-known methods.

Accordingly, in certain embodiments the sample includes or consistsessentially of a purified or isolated polynucleotide, or it can includesamples such as a tissue sample, a biological fluid sample, a cellsample, and the like. Suitable biological fluid samples include, but arenot limited to blood, plasma, serum, sweat, tears, sputum, urine,sputum, ear flow, lymph, saliva, cerebrospinal fluid, ravages, bonemarrow suspension, vaginal flow, trans-cervical lavage, brain fluid,ascites, milk, secretions of the respiratory, intestinal andgenitourinary tracts, amniotic fluid, milk, and leukophoresis samples.In some embodiments, the sample is a sample that is easily obtainable bynon-invasive procedures, e.g., blood, plasma, serum, sweat, tears,sputum, urine, sputum, ear flow, saliva or feces. In certain embodimentsthe sample is a peripheral blood sample, or the plasma and/or serumfractions of a peripheral blood sample. In other embodiments, thebiological sample is a swab or smear, a biopsy specimen, or a cellculture. In another embodiment, the sample is a mixture of two or morebiological samples, e.g., a biological sample can include two or more ofa biological fluid sample, a tissue sample, and a cell culture sample.As used herein, the terms “blood,” “plasma” and “serum” expresslyencompass fractions or processed portions thereof. Similarly, where asample is taken from a biopsy, swab, smear, etc., the “sample” expresslyencompasses a processed fraction or portion derived from the biopsy,swab, smear, etc.

In certain embodiments, samples can be obtained from sources, including,but not limited to, samples from different individuals, samples fromdifferent developmental stages of the same or different individuals,samples from different diseased individuals (e.g., individuals suspectedof having a genetic disorder), normal individuals, samples obtained atdifferent stages of a disease in an individual, samples obtained from anindividual subjected to different treatments for a disease, samples fromindividuals subjected to different environmental factors, samples fromindividuals with predisposition to a pathology, samples individuals withexposure to an infectious disease agent, and the like.

In one illustrative, but non-limiting embodiment, the sample is amaternal sample that is obtained from a pregnant female, for example apregnant woman. In this instance, the sample can be analyzed using themethods described herein to provide a prenatal diagnosis of potentialchromosomal abnormalities in the fetus. The maternal sample can be atissue sample, a biological fluid sample, or a cell sample. A biologicalfluid includes, as non-limiting examples, blood, plasma, serum, sweat,tears, sputum, urine, sputum, ear flow, lymph, saliva, cerebrospinalfluid, ravages, bone marrow suspension, vaginal flow, transcervicallavage, brain fluid, ascites, milk, secretions of the respiratory,intestinal and genitourinary tracts, and leukophoresis samples.

In certain embodiments samples can also be obtained from in vitrocultured tissues, cells, or other polynucleotide-containing sources. Thecultured samples can be taken from sources including, but not limitedto, cultures (e.g., tissue or cells) maintained in different media andconditions (e.g., pH, pressure, or temperature), cultures (e.g., tissueor cells) maintained for different periods of length, cultures (e.g.,tissue or cells) treated with different factors or reagents (e.g., adrug candidate, or a modulator), or cultures of different types oftissue and/or cells.

Methods of isolating nucleic acids from biological sources are wellknown and will differ depending upon the nature of the source. One ofskill in the art can readily isolate nucleic acids from a source asneeded for the method described herein. In some instances, it can beadvantageous to fragment the nucleic acid molecules in the nucleic acidsample. Fragmentation can be random, or it can be specific, as achieved,for example, using restriction endonuclease digestion. Methods forrandom fragmentation are well known in the art, and include, forexample, limited DNAse digestion, alkali treatment and physicalshearing.

Sequencing Library Preparation

In various embodiments, sequencing may be performed on varioussequencing platforms that require preparation of a sequencing library.The preparation typically involves fragmenting the DNA (sonication,nebulization or shearing), followed by DNA repair and end polishing(blunt end or A overhang), and platform-specific adaptor ligation. Inone embodiment, the methods described herein can utilize next generationsequencing technologies (NGS), that allow multiple samples to besequenced individually as genomic molecules (i.e., singleplexsequencing) or as pooled samples comprising indexed genomic molecules(e.g., multiplex sequencing) on a single sequencing run. These methodscan generate up to several hundred million reads of DNA sequences. Invarious embodiments the sequences of genomic nucleic acids, and/or ofindexed genomic nucleic acids can be determined using, for example, theNext Generation Sequencing Technologies (NGS) described herein. Invarious embodiments analysis of the massive amount of sequence dataobtained using NGS can be performed using one or more processors asdescribed herein.

In various embodiments the use of such sequencing technologies does notinvolve the preparation of sequencing libraries.

However, in certain embodiments the sequencing methods contemplatedherein involve the preparation of sequencing libraries. In oneillustrative approach, sequencing library preparation involves theproduction of a random collection of adapter-modified DNA fragments(e.g., polynucleotides) that are ready to be sequenced. Sequencinglibraries of polynucleotides can be prepared from DNA or RNA, includingequivalents, analogs of either DNA or cDNA, for example, DNA or cDNAthat is complementary or copy DNA produced from an RNA template, by theaction of reverse transcriptase. The polynucleotides may originate indouble-stranded form (e.g., dsDNA such as genomic DNA fragments, cDNA,PCR amplification products, and the like) or, in certain embodiments,the polynucleotides may originated in single-stranded form (e.g., ssDNA,RNA, etc.) and have been converted to dsDNA form. By way ofillustration, in certain embodiments, single stranded mRNA molecules maybe copied into double-stranded cDNAs suitable for use in preparing asequencing library. The precise sequence of the primary polynucleotidemolecules is generally not material to the method of librarypreparation, and may be known or unknown. In one embodiment, thepolynucleotide molecules are DNA molecules. More particularly, incertain embodiments, the polynucleotide molecules represent the entiregenetic complement of an organism or substantially the entire geneticcomplement of an organism, and are genomic DNA molecules (e.g., cellularDNA, cell free DNA (cfDNA), etc.), that typically include both intronsequence and exon sequence (coding sequence), as well as non-codingregulatory sequences such as promoter and enhancer sequences. In certainembodiments, the primary polynucleotide molecules comprise human genomicDNA molecules, e.g., cfDNA molecules present in peripheral blood of apregnant subject.

Preparation of sequencing libraries for some NGS sequencing platforms isfacilitated by the use of polynucleotides comprising a specific range offragment sizes. Preparation of such libraries typically involves thefragmentation of large polynucleotides (e.g. cellular genomic DNA) toobtain polynucleotides in the desired size range.

Paired end reads are used for the methods and systems disclosed hereinfor determining repeat expansion. The fragment or insert length islonger than the read length, and typically longer than the sum of thelengths of the two reads.

In some illustrative embodiments, the sample nucleic acid(s) areobtained as genomic DNA, which is subjected to fragmentation intofragments of approximately 100 or more, approximately 200 or more,approximately 300 or more, approximately 400 or more, or approximately500 or more base pairs, and to which NGS methods can be readily applied.In some embodiments, the paired end reads are obtained from inserts ofabout 100-5000 bp. In some embodiments, the inserts are about 100-1000bp long. These are sometimes implemented as regular short-insert pairedend reads. In some embodiments, the inserts are about 1000-5000 bp long.These are sometimes implemented as long-insert mate paired reads asdescribed above.

In some implementations, long inserts are designed for evaluating verylong, expanded repeat sequences. In some implementations, mate pairreads may be applied to obtain reads that are spaced apart by thousandsof base pairs. In these implementations, inserts or fragments range fromhundreds to thousands of base pairs, with two biotin junction adaptorson the two ends of an insert. Then the biotin junction adaptors join thetwo ends of the insert to form a circularized molecule, which is thenfurther fragmented. A sub-fragment including the biotin junctionadaptors and the two ends of the original insert is selected forsequencing on a platform that is designed to sequence shorter fragments.

Fragmentation can be achieved by any of a number of methods known tothose of skill in the art. For example, fragmentation can be achieved bymechanical means including, but not limited to nebulization, sonicationand hydroshear. However mechanical fragmentation typically cleaves theDNA backbone at C—O, P—O and C—C bonds resulting in a heterogeneous mixof blunt and 3′- and 5′-overhanging ends with broken C—O, P—O and/ C—Cbonds (see, e.g., Alnemri and Liwack, J Biol. Chem 265:17323-17333[1990]; Richards and Boyer, J Mol Biol 11:327-240 [1965]) which may needto be repaired as they may lack the requisite 5′-phosphate for thesubsequent enzymatic reactions, e.g., ligation of sequencing adaptors,that are required for preparing DNA for sequencing.

In contrast, cfDNA, typically exists as fragments of less than about 300base pairs and consequently, fragmentation is not typically necessaryfor generating a sequencing library using cfDNA samples.

Typically, whether polynucleotides are forcibly fragmented (e.g.,fragmented in vitro), or naturally exist as fragments, they areconverted to blunt-ended DNA having 5′-phosphates and 3′-hydroxyl.Standard protocols, e.g., protocols for sequencing using, for example,the Illumina platform as described elsewhere herein, instruct users toend-repair sample DNA, to purify the end-repaired products prior todA-tailing, and to purify the dA-tailing products prior to theadaptor-ligating steps of the library preparation.

Various embodiments of methods of sequence library preparation describedherein obviate the need to perform one or more of the steps typicallymandated by standard protocols to obtain a modified DNA product that canbe sequenced by NGS. An abbreviated method (ABB method), a 1-stepmethod, and a 2-step method are examples of methods for preparation of asequencing library, which can be found in patent application Ser. No.13/555,037 filed on Jul. 20, 2012, which is incorporated by reference byits entirety.

Sequencing Methods

As indicated above, the prepared samples (e.g., Sequencing Libraries)are sequenced as part of the procedure for identifying copy numbervariation(s). Any of a number of sequencing technologies can beutilized.

Some sequencing technologies are available commercially, such as thesequencing-by-hybridization platform from Affymetrix Inc. (Sunnyvale,Calif.) and the sequencing-by-synthesis platforms from 454 Life Sciences(Bradford, Conn.), Illumina/Solexa (San Diego, Calif.) and HelicosBiosciences (Cambridge, Mass.), and the sequencing-by-ligation platformfrom Applied Biosystems (Foster City, Calif.), as described below. Inaddition to the single molecule sequencing performed usingsequencing-by-synthesis of Helicos Biosciences, other single moleculesequencing technologies include, but are not limited to, the SMRT™technology of Pacific Biosciences, the ION TORRENT™ technology, andnanopore sequencing developed for example, by Oxford NanoporeTechnologies.

While the automated Sanger method is considered as a ‘first generation’technology, Sanger sequencing including the automated Sanger sequencing,can also be employed in the methods described herein. Additionalsuitable sequencing methods include, but are not limited to nucleic acidimaging technologies, e.g., atomic force microscopy (AFM) ortransmission electron microscopy (TEM). Illustrative sequencingtechnologies are described in greater detail below.

In some embodiments, the disclosed methods involve obtaining sequenceinformation for the nucleic acids in the test sample by massivelyparallel sequencing of millions of DNA fragments using Illumina'ssequencing-by-synthesis and reversible terminator-based sequencingchemistry (e.g. as described in Bentley et al., Nature 6:53-59 [2009]).Template DNA can be genomic DNA, e.g., cellular DNA or cfDNA. In someembodiments, genomic DNA from isolated cells is used as the template,and it is fragmented into lengths of several hundred base pairs. Inother embodiments, cfDNA is used as the template, and fragmentation isnot required as cfDNA exists as short fragments. For example fetal cfDNAcirculates in the bloodstream as fragments approximately 170 base pairs(bp) in length (Fan et al., Clin Chem 56:1279-1286 [2010]), and nofragmentation of the DNA is required prior to sequencing. Illumina'ssequencing technology relies on the attachment of fragmented genomic DNAto a planar, optically transparent surface on which oligonucleotideanchors are bound. Template DNA is end-repaired to generate5′-phosphorylated blunt ends, and the polymerase activity of Klenowfragment is used to add a single A base to the 3′ end of the bluntphosphorylated DNA fragments. This addition prepares the DNA fragmentsfor ligation to oligonucleotide adapters, which have an overhang of asingle T base at their 3′ end to increase ligation efficiency. Theadapter oligonucleotides are complementary to the flow-cell anchoroligos (not to be confused with the anchor/anchored reads in theanalysis of repeat expansion). Under limiting-dilution conditions,adapter-modified, single-stranded template DNA is added to the flow celland immobilized by hybridization to the anchor oligos. Attached DNAfragments are extended and bridge amplified to create an ultra-highdensity sequencing flow cell with hundreds of millions of clusters, eachcontaining about 1,000 copies of the same template. In one embodiment,the randomly fragmented genomic DNA is amplified using PCR before it issubjected to cluster amplification. Alternatively, an amplification-freegenomic library preparation is used, and the randomly fragmented genomicDNA is enriched using the cluster amplification alone (Kozarewa et al.,Nature Methods 6:291-295 [2009]). The templates are sequenced using arobust four-color DNA sequencing-by-synthesis technology that employsreversible terminators with removable fluorescent dyes. High-sensitivityfluorescence detection is achieved using laser excitation and totalinternal reflection optics. Short sequence reads of about tens to a fewhundred base pairs are aligned against a reference genome and uniquemapping of the short sequence reads to the reference genome areidentified using specially developed data analysis pipeline software.After completion of the first read, the templates can be regenerated insitu to enable a second read from the opposite end of the fragments.Thus, either single-end or paired end sequencing of the DNA fragmentscan be used.

Various embodiments of the disclosure may use sequencing by synthesisthat allows paired end sequencing. In some embodiments, the sequencingby synthesis platform by Illumina involves clustering fragments.Clustering is a process in which each fragment molecule is isothermallyamplified. In some embodiments, as the example described here, thefragment has two different adaptors attached to the two ends of thefragment, the adaptors allowing the fragment to hybridize with the twodifferent oligos on the surface of a flow cell lane. The fragmentfurther includes or is connected to two index sequences at two ends ofthe fragment, which index sequences provide labels to identify differentsamples in multiplex sequencing. In some sequencing platforms, afragment to be sequenced is also referred to as an insert.

In some implementation, a flow cell for clustering in the Illuminaplatform is a glass slide with lanes. Each lane is a glass channelcoated with a lawn of two types of oligos. Hybridization is enabled bythe first of the two types of oligos on the surface. This oligo iscomplementary to a first adapter on one end of the fragment. Apolymerase creates a complement strand of the hybridized fragment. Thedouble-stranded molecule is denatured, and the original template strandis washed away. The remaining strand, in parallel with many otherremaining strands, is clonally amplified through bridge application.

In bridge amplification, a second adapter region on a second end of thestrand hybridizes with the second type of oligos on the flow cellsurface. A polymerase generates a complementary strand, forming adouble-stranded bridge molecule. This double-stranded molecule isdenatured resulting in two single-stranded molecules tethered to theflow cell through two different oligos. The process is then repeatedover and over, and occurs simultaneously for millions of clustersresulting in clonal amplification of all the fragments. After bridgeamplification, the reverse strands are cleaved and washed off, leavingonly the forward strands. The 3′ ends are blocked to prevent unwantedpriming.

After clustering, sequencing starts with extending a first sequencingprimer to generate the first read. With each cycle, fluorescently taggednucleotides compete for addition to the growing chain. Only one isincorporated based on the sequence of the template. After the additionof each nucleotide, the cluster is excited by a light source, and acharacteristic fluorescent signal is emitted. The number of cyclesdetermines the length of the read. The emission wavelength and thesignal intensity determine the base call. For a given cluster allidentical strands are read simultaneously. Hundreds of millions ofclusters are sequenced in a massively parallel manner At the completionof the first read, the read product is washed away.

In the next step of protocols involving two index primers, an index 1primer is introduced and hybridized to an index 1 region on thetemplate. Index regions provide identification of fragments, which isuseful for de-multiplexing samples in a multiplex sequencing process.The index 1 read is generated similar to the first read. Aftercompletion of the index 1 read, the read product is washed away and the3′ end of the strand is de-protected. The template strand then foldsover and binds to a second oligo on the flow cell. An index 2 sequenceis read in the same manner as index 1. Then an index 2 read product iswashed off at the completion of the step.

After reading two indices, read 2 initiates by using polymerases toextend the second flow cell oligos, forming a double-stranded bridge.This double-stranded DNA is denatured, and the 3′ end is blocked. Theoriginal forward strand is cleaved off and washed away, leaving thereverse strand. Read 2 begins with the introduction of a read 2sequencing primer. As with read 1, the sequencing steps are repeateduntil the desired length is achieved. The read 2 product is washed away.This entire process generates millions of reads, representing all thefragments. Sequences from pooled sample libraries are separated based onthe unique indices introduced during sample preparation. For eachsample, reads of similar stretches of base calls are locally clustered.Forward and reversed reads are paired creating contiguous sequences.These contiguous sequences are aligned to the reference genome forvariant identification.

The sequencing by synthesis example described above involves paired endreads, which is used in many of the embodiments of the disclosedmethods. Paired end sequencing involves 2 reads from the two ends of afragment. Paired end reads are used to resolve ambiguous alignments.Paired-end sequencing allows users to choose the length of the insert(or the fragment to be sequenced) and sequence either end of the insert,generating high-quality, alignable sequence data. Because the distancebetween each paired read is known, alignment algorithms can use thisinformation to map reads over repetitive regions more precisely. Thisresults in better alignment of the reads, especially acrossdifficult-to-sequence, repetitive regions of the genome. Paired-endsequencing can detect rearrangements, including insertions and deletions(indels) and inversions.

Paired end reads may use insert of different length (i.e., differentfragment size to be sequenced). As the default meaning in thisdisclosure, paired end reads are used to refer to reads obtained fromvarious insert lengths. In some instances, to distinguish short-insertpaired end reads from long-inserts paired end reads, the latter isspecifically referred to as mate pair reads. In some embodimentsinvolving mate pair reads, two biotin junction adaptors first areattached to two ends of a relatively long insert (e.g., several kb). Thebiotin junction adaptors then link the two ends of the insert to form acircularized molecule. A sub-fragment encompassing the biotin junctionadaptors can then be obtained by further fragmenting the circularizedmolecule. The sub-fragment including the two ends of the originalfragment in opposite sequence order can then be sequenced by the sameprocedure as for short-insert paired end sequencing described above.Further details of mate pair sequencing using an Illumina platform isshown in an online publication at the following address, which isincorporated by reference by its entirety:res.illumina.com/documents/products/technotes/technote_nextera_matepair_data_processing.pdf

After sequencing of DNA fragments, sequence reads of predeterminedlength, e.g., 100 bp, are mapped or aligned to a known reference genome.The mapped or aligned reads and their corresponding locations on thereference sequence are also referred to as tags. The analyses of manyembodiments disclosed herein for determining repeat expansion make useof reads that are either poorly aligned or cannot be aligned, as well asaligned reads (tags). In one embodiment, the reference genome sequenceis the NCBI36/hg18 sequence, which is available on the world wide web atgenome.ucsc.edu/cgi-bin/hgGateway?org=Human&db=hg18&hgsid=166260105).Alternatively, the reference genome sequence is the GRCh37/hd19, whichis available on the world wide web at genome.ucsc.edu/egi-bin/hgGateway.Other sources of public sequence information include GenBank, dbEST,dbSTS, EMBL (the European Molecular Biology Laboratory), and the DDBJ(the DNA Databank of Japan). A number of computer algorithms areavailable for aligning sequences, including without limitation BLAST(Altschul et al., 1990), BLITZ (MPsrch) (Sturrock & Collins, 1993),FASTA (Person & Lipman, 1988), BOWTIE (Langmead et al., Genome Biology10:R25.1-R25.10 [2009]), or ELAND (Illumina, Inc., San Diego, Calif.,USA). In one embodiment, one end of the clonally expanded copies of theplasma cfDNA molecules is sequenced and processed by bioinformaticalignment analysis for the Illumina Genome Analyzer, which uses theEfficient Large-Scale Alignment of Nucleotide Databases (ELAND)software.

In one illustrative, but non-limiting, embodiment, the methods describedherein comprise obtaining sequence information for the nucleic acids ina test sample, using single molecule sequencing technology of theHelicos True Single Molecule Sequencing (tSMS) technology (e.g. asdescribed in Harris T.D. et al., Science 320:106-109 [2008]). In thetSMS technique, a DNA sample is cleaved into strands of approximately100 to 200 nucleotides, and a polyA sequence is added to the 3′ end ofeach DNA strand. Each strand is labeled by the addition of afluorescently labeled adenosine nucleotide. The DNA strands are thenhybridized to a flow cell, which contains millions of oligo-T capturesites that are immobilized to the flow cell surface. In certainembodiments the templates can be at a density of about 100 milliontemplates/cm². The flow cell is then loaded into an instrument, e.g.,HeliScope™ sequencer, and a laser illuminates the surface of the flowcell, revealing the position of each template. A CCD camera can map theposition of the templates on the flow cell surface. The templatefluorescent label is then cleaved and washed away. The sequencingreaction begins by introducing a DNA polymerase and a fluorescentlylabeled nucleotide. The oligo-T nucleic acid serves as a primer. Thepolymerase incorporates the labeled nucleotides to the primer in atemplate directed manner The polymerase and unincorporated nucleotidesare removed. The templates that have directed incorporation of thefluorescently labeled nucleotide are discerned by imaging the flow cellsurface. After imaging, a cleavage step removes the fluorescent label,and the process is repeated with other fluorescently labeled nucleotidesuntil the desired read length is achieved. Sequence information iscollected with each nucleotide addition step. Whole genome sequencing bysingle molecule sequencing technologies excludes or typically obviatesPCR-based amplification in the preparation of the sequencing libraries,and the methods allow for direct measurement of the sample, rather thanmeasurement of copies of that sample.

In another illustrative, but non-limiting embodiment, the methodsdescribed herein comprise obtaining sequence information for the nucleicacids in the test sample, using the 454 sequencing (Roche) (e.g. asdescribed in Margulies, M. et al. Nature 437:376-380 [2005]). 454sequencing typically involves two steps. In the first step, DNA issheared into fragments of approximately 300-800 base pairs, and thefragments are blunt-ended. Oligonucleotide adaptors are then ligated tothe ends of the fragments. The adaptors serve as primers foramplification and sequencing of the fragments. The fragments can beattached to DNA capture beads, e.g., streptavidin-coated beads using,e.g., Adaptor B, which contains 5′-biotin tag. The fragments attached tothe beads are PCR amplified within droplets of an oil-water emulsion.The result is multiple copies of clonally amplified DNA fragments oneach bead. In the second step, the beads are captured in wells (e.g.,picoliter-sized wells). Pyrosequencing is performed on each DNA fragmentin parallel. Addition of one or more nucleotides generates a lightsignal that is recorded by a CCD camera in a sequencing instrument. Thesignal strength is proportional to the number of nucleotidesincorporated. Pyrosequencing makes use of pyrophosphate (PPi) which isreleased upon nucleotide addition. PPi is converted to ATP by ATPsulfurylase in the presence of adenosine 5′ phosphosulfate. Luciferaseuses ATP to convert luciferin to oxyluciferin, and this reactiongenerates light that is measured and analyzed.

In another illustrative, but non-limiting, embodiment, the methodsdescribed herein comprises obtaining sequence information for thenucleic acids in the test sample, using the SOLiD™ technology (AppliedBiosystems). In SOLiD™ sequencing-by-ligation, genomic DNA is shearedinto fragments, and adaptors are attached to the 5′ and 3′ ends of thefragments to generate a fragment library. Alternatively, internaladaptors can be introduced by ligating adaptors to the 5′ and 3′ ends ofthe fragments, circularizing the fragments, digesting the circularizedfragment to generate an internal adaptor, and attaching adaptors to the5′ and 3′ ends of the resulting fragments to generate a mate-pairedlibrary. Next, clonal bead populations are prepared in microreactorscontaining beads, primers, template, and PCR components. Following PCR,the templates are denatured and beads are enriched to separate the beadswith extended templates. Templates on the selected beads are subjectedto a 3′ modification that permits bonding to a glass slide. The sequencecan be determined by sequential hybridization and ligation of partiallyrandom oligonucleotides with a central determined base (or pair ofbases) that is identified by a specific fluorophore. After a color isrecorded, the ligated oligonucleotide is cleaved and removed and theprocess is then repeated.

In another illustrative, but non-limiting, embodiment, the methodsdescribed herein comprise obtaining sequence information for the nucleicacids in the test sample, using the single molecule, real-time (SMRT™)sequencing technology of Pacific Biosciences. In SMRT sequencing, thecontinuous incorporation of dye-labeled nucleotides is imaged during DNAsynthesis. Single DNA polymerase molecules are attached to the bottomsurface of individual zero-mode wavelength detectors (ZMW detectors)that obtain sequence information while phospholinked nucleotides arebeing incorporated into the growing primer strand. A ZMW detectorcomprises a confinement structure that enables observation ofincorporation of a single nucleotide by DNA polymerase against abackground of fluorescent nucleotides that rapidly diffuse in an out ofthe ZMW (e.g., in microseconds). It typically takes several millisecondsto incorporate a nucleotide into a growing strand. During this time, thefluorescent label is excited and produces a fluorescent signal, and thefluorescent tag is cleaved off. Measurement of the correspondingfluorescence of the dye indicates which base was incorporated. Theprocess is repeated to provide a sequence.

In another illustrative, but non-limiting embodiment, the methodsdescribed herein comprise obtaining sequence information for the nucleicacids in the test sample, using nanopore sequencing (e.g. as describedin Soni G V and Meller A. Clin Chem 53: 1996-2001 [2007]). Nanoporesequencing DNA analysis techniques are developed by a number ofcompanies, including, for example, Oxford Nanopore Technologies (Oxford,United Kingdom), Sequenom, NABsys, and the like. Nanopore sequencing isa single-molecule sequencing technology whereby a single molecule of DNAis sequenced directly as it passes through a nanopore. A nanopore is asmall hole, typically of the order of 1 nanometer in diameter. Immersionof a nanopore in a conducting fluid and application of a potential(voltage) across it results in a slight electrical current due toconduction of ions through the nanopore. The amount of current thatflows is sensitive to the size and shape of the nanopore. As a DNAmolecule passes through a nanopore, each nucleotide on the DNA moleculeobstructs the nanopore to a different degree, changing the magnitude ofthe current through the nanopore in different degrees. Thus, this changein the current as the DNA molecule passes through the nanopore providesa read of the DNA sequence.

In another illustrative, but non-limiting, embodiment, the methodsdescribed herein comprises obtaining sequence information for thenucleic acids in the test sample, using the chemical-sensitive fieldeffect transistor (chemFET) array (e.g., as described in U.S. PatentApplication Publication No. 2009/0026082). In one example of thistechnique, DNA molecules can be placed into reaction chambers, and thetemplate molecules can be hybridized to a sequencing primer bound to apolymerase. Incorporation of one or more triphosphates into a newnucleic acid strand at the 3′ end of the sequencing primer can bediscerned as a change in current by a chemFET. An array can havemultiple chemFET sensors. In another example, single nucleic acids canbe attached to beads, and the nucleic acids can be amplified on thebead, and the individual beads can be transferred to individual reactionchambers on a chemFET array, with each chamber having a chemFET sensor,and the nucleic acids can be sequenced.

In another embodiment, the DNA sequencing technology is the Ion Torrentsingle molecule sequencing, which pairs semiconductor technology with asimple sequencing chemistry to directly translate chemically encodedinformation (A, C, G, T) into digital information (0, 1) on asemiconductor chip. In nature, when a nucleotide is incorporated into astrand of DNA by a polymerase, a hydrogen ion is released as abyproduct. Ion Torrent uses a high-density array of micro-machined wellsto perform this biochemical process in a massively parallel way. Eachwell holds a different DNA molecule. Beneath the wells is anion-sensitive layer and beneath that an ion sensor. When a nucleotide,for example a C, is added to a DNA template and is then incorporatedinto a strand of DNA, a hydrogen ion will be released. The charge fromthat ion will change the pH of the solution, which can be detected byIon Torrent's ion sensor. The sequencer—essentially the world's smallestsolid-state pH meter—calls the base, going directly from chemicalinformation to digital information. The Ion personal Genome Machine(PGM™) sequencer then sequentially floods the chip with one nucleotideafter another. If the next nucleotide that floods the chip is not amatch. No voltage change will be recorded and no base will be called. Ifthere are two identical bases on the DNA strand, the voltage will bedouble, and the chip will record two identical bases called. Directdetection allows recordation of nucleotide incorporation in seconds.

In another embodiment, the present method comprises obtaining sequenceinformation for the nucleic acids in the test sample, using sequencingby hybridization. Sequencing-by-hybridization comprises contacting theplurality of polynucleotide sequences with a plurality of polynucleotideprobes, wherein each of the plurality of polynucleotide probes can beoptionally tethered to a substrate. The substrate might be flat surfacecomprising an array of known nucleotide sequences. The pattern ofhybridization to the array can be used to determine the polynucleotidesequences present in the sample. In other embodiments, each probe istethered to a bead, e.g., a magnetic bead or the like. Hybridization tothe beads can be determined and used to identify the plurality ofpolynucleotide sequences within the sample.

In some embodiments of the methods described herein, the sequence readsare about 20 bp, about 25 bp, about 30 bp, about 35 bp, about 40 bp,about 45 bp, about 50 bp, about 55 bp, about 60 bp, about 65 bp, about70 bp, about 75 bp, about 80 bp, about 85 bp, about90 bp, about 95 bp,about 100 bp, about 110 bp, about 120 bp, about 130, about 140 bp, about150 bp, about 200 bp, about 250 bp, about 300 bp, about 350 bp, about400 bp, about 450 bp, or about 500 bp. It is expected that technologicaladvances will enable single-end reads of greater than 500 bp enablingfor reads of greater than about 1000 bp when paired end reads aregenerated. In some embodiments, paired end reads are used to determinerepeat expansion, which comprise sequence reads that are about 20 bp to1000 bp, about 50 bp to 500 bp, or 80 bp to 150 bp. In variousembodiments, the paired end reads are used to evaluate a sequence havinga repeat expansion. The sequence having the repeat expansion is longerthan the reads. In some embodiments, the sequence having the repeatexpansion is longer than about 100 bp, 500 bp, 1000 bp, or 4000 bp.Mapping of the sequence reads is achieved by comparing the sequence ofthe reads with the sequence of the reference to determine thechromosomal origin of the sequenced nucleic acid molecule, and specificgenetic sequence information is not needed. A small degree of mismatch(0-2 mismatches per read) may be allowed to account for minorpolymorphisms that may exist between the reference genome and thegenomes in the mixed sample. In some embodiments, reads that are alignedto the reference sequence are used as anchor reads, and reads paired toanchor reads but cannot align or poorly align to the reference are usedas anchored reads. In some embodiments, poorly aligned reads may have arelatively large number of percentage of mismatches per read, e.g., atleast about 5%, at least about 10%, at least about 15%, or at leastabout 20% mismatches per read.

A plurality of sequence tags (i.e., reads aligned to a referencesequence) are typically obtained per sample. In some embodiments, atleast about 3×10⁶ sequence tags, at least about 5×10⁶ sequence tags, atleast about 8×10⁶ sequence tags, at least about 10×10⁶ sequence tags, atleast about 15×10⁶ sequence tags, at least about 20×10⁶ sequence tags,at least about 30×10⁶ sequence tags, at least about 40×10⁶ sequencetags, or at least about 50 x 10⁶ sequence tags of, e.g., 100 bp, areobtained from mapping the reads to the reference genome per sample. Insome embodiments, all the sequence reads are mapped to all regions ofthe reference genome, providing genome-wide reads. In other embodiments,reads mapped to a sequence of interest, e.g., a chromosome, a segment ofa chromosome, or a repeat sequence of interest.

Apparatus and Systems for Determining Repeat Expansion

Analysis of the sequencing data and the diagnosis derived therefrom aretypically performed using various computer executed algorithms andprograms Therefore, certain embodiments employ processes involving datastored in or transferred through one or more computer systems or otherprocessing systems. Embodiments disclosed herein also relate toapparatus for performing these operations. This apparatus may bespecially constructed for the required purposes, or it may be ageneral-purpose computer (or a group of computers) selectively activatedor reconfigured by a computer program and/or data structure stored inthe computer. In some embodiments, a group of processors performs someor all of the recited analytical operations collaboratively (e.g., via anetwork or cloud computing) and/or in parallel. A processor or group ofprocessors for performing the methods described herein may be of varioustypes including microcontrollers and microprocessors such asprogrammable devices (e.g., CPLDs and FPGAs) and non-programmabledevices such as gate array ASICs or general purpose microprocessors.

One embodiment provides a system for use in determining the presence orabsence of a repeat expansion in a test sample including nucleic acids,the system including a sequencer for receiving a nucleic acid sample andproviding nucleic acid sequence information from the sample; aprocessor; and a machine readable storage medium having stored thereoninstructions for execution on said processor to evaluate copy number inthe test sample by: (a) aligning paired end reads to a referencesequence comprising the repeat sequence; (b) identifying anchor andanchored reads in the paired end reads, wherein the anchor reads arereads aligned to or near the repeat sequence, and the anchored reads areunaligned reads that are paired with the anchor reads; and (c)determining if the repeat expansion is likely to be present in the testsample based at least in part on the identified anchored reads. In someembodiments, (c) involves determining if the repeat expansion is likelyto be present in the test sample based at least in part on the numbersof repeats of the repeat unit in the identified anchor and/or anchoredreads. In some embodiments, (c) involves: obtaining the number of anchorand/or anchored reads that are high-count reads, wherein the high-countreads comprise reads having more repeats than a threshold value; andcomparing the number of high-count reads in the test sample to a callcriterion.

In some embodiments of any of the systems provided herein, the sequenceris configured to perform next generation sequencing (NGS). In someembodiments, the sequencer is configured to perform massively parallelsequencing using sequencing-by-synthesis with reversible dyeterminators. In other embodiments, the sequencer is configured toperform sequencing-by-ligation. In yet other embodiments, the sequenceris configured to perform single molecule sequencing.

In addition, certain embodiments relate to tangible and/ornon-transitory computer readable media or computer program products thatinclude program instructions and/or data (including data structures) forperforming various computer-implemented operations. Examples ofcomputer-readable media include, but are not limited to, semiconductormemory devices, magnetic media such as disk drives, magnetic tape,optical media such as CDs, magneto-optical media, and hardware devicesthat are specially configured to store and perform program instructions,such as read-only memory devices (ROM) and random access memory (RAM).The computer readable media may be directly controlled by an end user orthe media may be indirectly controlled by the end user. Examples ofdirectly controlled media include the media located at a user facilityand/or media that are not shared with other entities. Examples ofindirectly controlled media include media that is indirectly accessibleto the user via an external network and/or via a service providingshared resources such as the “cloud.” Examples of program instructionsinclude both machine code, such as produced by a compiler, and filescontaining higher level code that may be executed by the computer usingan interpreter.

In various embodiments, the data or information employed in thedisclosed methods and apparatus is provided in an electronic format.Such data or information may include reads and tags derived from anucleic acid sample, reference sequences (including reference sequencesproviding solely or primarily polymorphisms), calls such as repeatexpansion calls, counseling recommendations, diagnoses, and the like. Asused herein, data or other information provided in electronic format isavailable for storage on a machine and transmission between machines.Conventionally, data in electronic format is provided digitally and maybe stored as bits and/or bytes in various data structures, lists,databases, etc. The data may be embodied electronically, optically, etc.

One embodiment provides a computer program product for generating anoutput indicating the presence or absence of a repeat expansion in atest sample. The computer product may contain instructions forperforming any one or more of the above-described methods fordetermining a repeat expansion. As explained, the computer product mayinclude a non-transitory and/or tangible computer readable medium havinga computer executable or compilable logic (e.g., instructions) recordedthereon for enabling a processor to determine anchored read and repeatsin anchored reads, and whether a repeat expansion is present or absent.In one example, the computer product comprises a computer readablemedium having a computer executable or compilable logic (e.g.,instructions) recorded thereon for enabling a processor to diagnose arepeat expansion comprising: a receiving procedure for receivingsequencing data from at least a portion of nucleic acid molecules from abiological sample, wherein said sequencing data comprises paired endreads that have undergone alignment to a repeat sequence; computerassisted logic for analyzing a repeat expansion from said received data;and an output procedure for generating an output indicating thepresence, absence or kind of said repeat expansion.

The sequence information from the sample under consideration may bemapped to chromosome reference sequences to identify paired end readsaligned to or anchored to a repeat sequence of interest and to identifya repeat expansion of the repeat sequence. In various embodiments, thereference sequences are stored in a database such as a relational orobject database.

It should be understood that it is not practical, or even possible inmost cases, for an unaided human being to perform the computationaloperations of the methods disclosed herein. For example, mapping asingle 30 bp read from a sample to any one of the human chromosomesmight require years of effort without the assistance of a computationalapparatus. Of course, the problem is compounded because reliable repeatexpansion calls generally require mapping thousands (e.g., at leastabout 10,000) or even millions of reads to one or more chromosomes.

The methods disclosed herein can be performed using a system forevaluation of repeat expansion of a repeat sequence of interest in atest sample. The system may include: (a) a sequencer for receivingnucleic acids from the test sample providing nucleic acid sequenceinformation from the sample; (b) a processor; and (c) one or morecomputer-readable storage media having stored thereon instructions forexecution on said processor to evaluate a repeat expansion in the testsample. In some embodiments, the methods are instructed by acomputer-readable medium having stored thereon computer-readableinstructions for carrying out a method for identifying any repeatexpansion. Thus one embodiment provides a computer program productincluding a non-transitory machine readable medium storing program codethat, when executed by one or more processors of a computer system,causes the computer system to implement a method for identifying arepeat expansion of a repeat sequence in a test sample including nucleicacids, wherein the repeat sequence includes repeats of a repeat unit ofnucleotides. The program code may include: (a) code for obtaining pairedend reads of the test sample that have been have been processed to alignto a reference sequence comprising the repeat sequence; (b) code foridentifying anchor and/or anchored reads in the paired end reads,wherein the anchor reads are reads aligned to or near the repeatsequence, and the anchored reads are unaligned reads that are pairedwith the anchor reads; and (c) code for determining if the repeatexpansion is likely to be present in the test sample based at least inpart on the identified anchor reads and/or anchored reads.

In some embodiments, (c) includes codes for analyzing both anchor andanchored reads. In some embodiments, (c) includes codes for analyzingthe numbers of repeats of the repeat unit in the identified anchorand/or anchored reads. In some embodiments, (c) includes codes forobtaining the number of anchor and/or anchored reads that are high-countreads, and comparing the number of high-count reads in the test sampleto a call criterion.

In some embodiments, the instructions may further include automaticallyrecording information pertinent to the method such as repeats andanchored reads, and the presence or absence of a repeat expansion in apatient medical record for a human subject providing the test sample.The patient medical record may be maintained by, for example, alaboratory, physician's office, a hospital, a health maintenanceorganization, an insurance company, or a personal medical recordwebsite. Further, based on the results of the processor-implementedanalysis, the method may further involve prescribing, initiating, and/oraltering treatment of a human subject from whom the test sample wastaken. This may involve performing one or more additional tests oranalyses on additional samples taken from the subject.

Disclosed methods can also be performed using a computer processingsystem which is adapted or configured to perform a method foridentifying any repeat expansions. One embodiment provides a computerprocessing system which is adapted or configured to perform a method asdescribed herein. In one embodiment, the apparatus includes a sequencingdevice adapted or configured for sequencing at least a portion of thenucleic acid molecules in a sample to obtain the type of sequenceinformation described elsewhere herein. The apparatus may also includecomponents for processing the sample. Such components are describedelsewhere herein.

Sequence or other data, can be input into a computer or stored on acomputer readable medium either directly or indirectly. In oneembodiment, a computer system is directly coupled to a sequencing devicethat reads and/or analyzes sequences of nucleic acids from samples.Sequences or other information from such tools are provided viainterface in the computer system. Alternatively, the sequences processedby system are provided from a sequence storage source such as a databaseor other repository. Once available to the processing apparatus, amemory device or mass storage device buffers or stores, at leasttemporarily, sequences of the nucleic acids. In addition, the memorydevice may store tag counts for various chromosomes or genomes, etc. Thememory may also store various routines and/or programs for analyzing thepresenting the sequence or mapped data. Such programs/routines mayinclude programs for performing statistical analyses, etc.

In one example, a user provides a sample into a sequencing apparatus.Data is collected and/or analyzed by the sequencing apparatus which isconnected to a computer. Software on the computer allows for datacollection and/or analysis. Data can be stored, displayed (via a monitoror other similar device), and/or sent to another location. The computermay be connected to the internet which is used to transmit data to ahandheld device utilized by a remote user (e.g., a physician, scientistor analyst). It is understood that the data can be stored and/oranalyzed prior to transmittal. In some embodiments, raw data iscollected and sent to a remote user or apparatus that will analyzeand/or store the data. Transmittal can occur via the internet, but canalso occur via satellite or other connection. Alternately, data can bestored on a computer-readable medium and the medium can be shipped to anend user (e.g., via mail). The remote user can be in the same or adifferent geographical location including, but not limited to abuilding, city, state, country or continent.

In some embodiments, the methods also include collecting data regardinga plurality of polynucleotide sequences (e.g., reads, tags and/orreference chromosome sequences) and sending the data to a computer orother computational system. For example, the computer can be connectedto laboratory equipment, e.g., a sample collection apparatus, anucleotide amplification apparatus, a nucleotide sequencing apparatus,or a hybridization apparatus. The computer can then collect applicabledata gathered by the laboratory device. The data can be stored on acomputer at any step, e.g., while collected in real time, prior to thesending, during or in conjunction with the sending, or following thesending. The data can be stored on a computer-readable medium that canbe extracted from the computer. The data collected or stored can betransmitted from the computer to a remote location, e.g., via a localnetwork or a wide area network such as the internet. At the remotelocation various operations can be performed on the transmitted data asdescribed below.

Among the types of electronically formatted data that may be stored,transmitted, analyzed, and/or manipulated in systems, apparatus, andmethods disclosed herein are the following:

-   -   Reads obtained by sequencing nucleic acids in a test sample    -   Tags obtained by aligning reads to a reference genome or other        reference sequence or sequences    -   The reference genome or sequence    -   Thresholds for calling a test sample as either affected,        non-affected, or no call    -   The actual calls of repeat expansion    -   Diagnoses (clinical condition associated with the calls)    -   Recommendations for further tests derived from the calls and/or        diagnoses    -   Treatment and/or monitoring plans derived from the calls and/or        diagnoses

These various types of data may be obtained, stored transmitted,analyzed, and/or manipulated at one or more locations using distinctapparatus. The processing options span a wide spectrum. At one end ofthe spectrum, all or much of this information is stored and used at thelocation where the test sample is processed, e.g., a doctor's office orother clinical setting. In other extreme, the sample is obtained at onelocation, it is processed and optionally sequenced at a differentlocation, reads are aligned and calls are made at one or more differentlocations, and diagnoses, recommendations, and/or plans are prepared atstill another location (which may be a location where the sample wasobtained).

In various embodiments, the reads are generated with the sequencingapparatus and then transmitted to a remote site where they are processedto produce repeat expansion calls. At this remote location, as anexample, the reads are aligned to a reference sequence to produce anchorand anchored reads. Among the processing operations that may be employedat distinct locations are the following:

-   -   Sample collection    -   Sample processing preliminary to sequencing    -   Sequencing    -   Analyzing sequence data and deriving repeat expansion calls    -   Diagnosis    -   Reporting a diagnosis and/or a call to patient or health care        provider    -   Developing a plan for further treatment, testing, and/or        monitoring    -   Executing the plan    -   Counseling

Any one or more of these operations may be automated as describedelsewhere herein. Typically, the sequencing and the analyzing ofsequence data and deriving repeat expansion calls will be performedcomputationally. The other operations may be performed manually orautomatically.

FIG. 6 shows one implementation of a dispersed system for producing acall or diagnosis from a test sample. A sample collection location 01 isused for obtaining a test sample from a patient. The samples thenprovided to a processing and sequencing location 03 where the testsample may be processed and sequenced as described above. Location 03includes apparatus for processing the sample as well as apparatus forsequencing the processed sample. The result of the sequencing, asdescribed elsewhere herein, is a collection of reads which are typicallyprovided in an electronic format and provided to a network such as theInternet, which is indicated by reference number 05 in FIG. 6.

The sequence data is provided to a remote location 07 where analysis andcall generation are performed. This location may include one or morepowerful computational devices such as computers or processors. Afterthe computational resources at location 07 have completed their analysisand generated a call from the sequence information received, the call isrelayed back to the network 05. In some implementations, not only is acall generated at location 07 but an associated diagnosis is alsogenerated. The call and or diagnosis are then transmitted across thenetwork and back to the sample collection location 01 as illustrated inFIG. 6. As explained, this is simply one of many variations on how thevarious operations associated with generating a call or diagnosis may bedivided among various locations. One common variant involves providingsample collection and processing and sequencing in a single location.Another variation involves providing processing and sequencing at thesame location as analysis and call generation.

EXPERIMENTAL EXAMPLE 1 Determining Repeat Expansion Related to Fragile XSyndrome

This example presents a study aimed to determine repeat expansionrelated to Fragile X syndrome using relatively short paired end reads of100 bp read length. Fragile X syndrome (FXS) is linked to a repeat unitof the CGG triplet in FMR1 on chromosome X. It is an X-linked trait withincidence 1 in 4,000 in males and 1 in 6,000 in females. When the repeatis smaller than 60 copies, the phenotype is usually normal, with themost common genes having about 30 repeats in the repeat sequence. Insome studies, 60-200 copies or repeats of the repeat unit ispre-mutation, which may lead to Fragile X tremor ataxia syndrome. Lateonset disorder of Fragile X is characterized by problems with movementand cognitive ability. The risk of expansion increases exponentiallywith the number of repeats above 65. More than 200 copies of the repeattriplet typically lead to Fragile X syndrome with mild to severe mentalretardation.

What is represented in the reference genome are (CGG)10+(AGG)+(CGG)9.The presence of the AGG in the repeat is thought to be protective bymaintaining the stability of the repeat. Tracts of over 30 adjacent CGGtriplets are thought to be more prone to expansion. Longer tractswithout an AGG are more prone to next-generation expansion.

While intuitively it may seem that reads must span the entire repeatsequence to determine if a sequenced individual has a medicallysignificant repeat expansion, reads much shorter than the repeatsequence may be used to determine if a repeat expansion is present.Using the methods disclosed herein, in the presence of a repeatexpansion, there is a large number of read pairs where one read alignsin the flanking sequence outside of the repeat and the other alignsentirely within the repeat. This either does not occur in normal samplesor only occurs in a small number of read pairs. Additionally, extremelylong repeat expansions will have a number of read pairs where each readis almost entirely composed of a repeat unit. These reads will end upbeing unaligned and should not exist in normal samples. The expectationfor each type of repeat sequence can be quantified by examining wholegenome sequencing data from a random group of normal samples.

Using the above-described methods, sequence data from two Fragile XSyndrome samples that have triplet repeats of 193 and 645 copies,respectively, was examined. As discussed above, a normal sample hasabout 30 repeats and repeat expansions having more than 60 repeats areconsidered medically relevant. In comparison to normal samples, anexcess of reads with a large number of the CGG repeat in the Fragile Xsamples was identified. Additionally, both Fragile X samples showed anexcess of read pairs with both reads showing the repeats. FIGS. 7-13,discussed below, show the comparison of the Fragile X samples withnormal samples for localized analysis and whole-genome analysis. AMann-Whitney rank test shows that the distributions of the Fragile Xsamples have significantly more repeats than the normal samples(p=2×10⁻⁷ and p=2×10⁻¹³).

In this example, for each sample, nucleic acids were extracted andpaired end sequencing was performed, followed by alignment to identifyanchor reads and anchored unaligned reads, the anchor reads being readsaligned to the reference sequence within 1 kb of the repeat sequence ofthe FMR1 gene, and the anchored reads being reads paired with an anchorread that could not be aligned or were aligned poorly. For every one ofthe anchor or anchored read in a sample, the number of in-frame CGGs wascalculated. Then the sample's distribution of numbers of in-frame CGGswas compared with a null distribution from randomly selected controlsamples to determine if there was an excess of reads with many CGGs. Thenull distribution was calculated from 1,013 unaffected samples.

Some of the analysis in this example involves two Fragile X samples. Oneis a female sample labeled as NA20239 (20+193), indicating repeat lengthof 20 copies on one chromosome and 193 on the other. The second FragileX sample is a male sample labeled as NA04025 (645), indicating repeatlength of 645 for the one copy of the gene on the single X chromosome.

FIG. 7 shows the distribution of CGG triplet counts in paired end readsaligned or anchored to the FMR1 gene from the 1,013 control samples.FIG. 7 shows the percentages of the different numbers of in-framerepeats. Because the read length is 100 bp, the maximum possible numberof repeats of the triplets for a read is 33. As apparent from the righthand side of FIG. 7, very few reads have 30 or more repeats. This may bedue to the protective effect of the AGG triplets limiting the maximumrepeats found in the normal samples. As mentioned above, in normalsamples, an AGG triplet is often found interspersed among CGG repeats,such as in the sequence (CGG)10+(AGG)+(CGG)9. The presence of the AGG inthe CGG repeats is thought to be protective by maintaining the stabilityof the sequence.

FIG. 8 shows the distribution of p values of the Mann-Whitney rank testfor the control samples. The MW rank test is a nonparametric statisticaltest, which compares an individual's ranked frequencies of repeats tothe ranked frequencies of the control samples, providing a p-valueindicating the probability of a false positive call. The figure showsthat only three of the 1,013 control samples have p<10⁻⁴ Therefore, thesame analysis using a p-value of 10⁻⁴ may be able to identify testsamples having repeat expansion of the FMR1 gene.

FIG. 9 shows the distribution of the numbers of repeats of the samplehaving the highest MW test score and the lowest p-value, p=2.7×10⁻⁵. Thehighest scoring sample's distribution is displayed alongside the controlsamples, with the highest scoring sample's distribution shown by hatchedbars and the control samples by solid black bars. The highest scoringsample has lower percentages of low count repeats, and higherpercentages of high count repeats. However, it does not have any readscontaining more than 30 repeats. This may be due to the presence of AGGtriplets in the repeat sequence.

FIG. 10 shows similar data for a female patient sample, NA20239(20+193), known to have the repeat expansion of the FMR1 gene andfragile X syndrome. The patient sample's data are shown in hatched barsand the controls' in solid black bars. The sample has 193 copies of theCGG triplet on one of the two X chromosomes. As shown on the right endof the figure, the sample has a large percentage of reads that have 31,32 or 33 repeats. The excess in this region is apparently due to readsfully within an expanded repeat sequence having few or no breaks. The MWtest shows that the sample has a p-value of 3.8×10⁻⁷.

FIG. 11 shows data for a male Fragile X patient sample, NA04025(645),having 645 copies of the CGG triplet on the X chromosome. The patientsample's data are shown in hatched bars and the controls' in solid blackbars. As shown on the right-hand side of the figure, this sample has aneven larger percentages of reads having 32 and 33 repeats than thefemale NA20239 patient sample. Over 30 percent of all of the reads have32 or 33 repeats. The MW test shows that the sample has a p-value of2.2×10⁻¹³.

Further analysis of the two patient samples along with other sampleshaving borderline large numbers of repeats revealed a gender bias usingthe data analysis approach described above. FIG. 12 shows the samedistribution of p values of the Mann-Whitney rank test for the controlsamples as FIG. 8, with the additional indication of four of the highestscoring female samples and four of the highest scoring male samples. Asthe arrows show, all four female samples have a −log 10(p-value) smallerthan eight, and all four male samples have a −log 10(p-value) largerthan eight. This is not surprising because the female samples have twocopies of the FMR1 genes, with one of the two copies having a normalnumber of repeats below 30. This copy of the normal FMR1 gene biases thedistribution of the female samples towards that of the control samples.

Using the methods described above, one can calculate the expected numberof partial repeats and full repeats given the length of a repeatsequence, a sequencing depth, and the length of the paired end reads.Table 2 lists the approximate expected numbers of partial repeats andfull repeats for various lengths of repeat sequences (shown as tripletcopies). Repeat sequences having a large number of repeat copies aremedically relevant, and affected female samples, which have two Xchromosomes, tend to have one long repeat sequence and one short repeatsequence.

TABLE 2 Approximate haploid repeat expectations vs triplet lengthTriplet Copies Partial Repeat* Full Repeat* 20 9 0 30 14 0 40 15 3 50 158 60 15 12 70 15 17 80 15 21 90 15 26 100 15 30 *Partial/full repeatindicates whether the read is partially or fully within the repeat

A new analysis was devised to zone in on long repeat sequences, such assequences having 100 copies of triplet, corresponding to 30 or morerepeats in a read. FIG. 13 shows that such an analysis indeed removesgender biases. FIG. 13 shows the numbers of samples having variousnumbers of high-count reads, where a high-count read is a readcontaining more than 29 CGG repeats. Strikingly, most of the controlsamples have very few high-count reads. More specifically, 828 of the1,013 control samples have zero high-count reads; 85 of the 1,013control samples have one; and 33 have two. Note that the three leftmostbars are truncated in the figure. The four highest scoring femalesamples and the four highest scoring male samples shown in FIG. 12 havethe largest numbers of high-count reads. These samples are indicated bythe hatched bars on the right end of the figure, having 18 to 30high-count reads. The control samples except for the highest scoringsamples are indicated by solid gray bars. More importantly, high scoringmale and female samples are inter-mixed in the 18 to 30 range. Based onthe empirical distribution of high-count reads of the control sample,and the expected numbers of full repeats for various triplet copies, adecision criterion can be chosen to differentiate normal repeat sequenceand repeat sequence having a pathogenic repeat expansion. For instance,60 triplet copies correspond to 12 full repeats. Using 12 as a cut offfor high-count reads, one can identify the one male and one female knownpatients, and seven highest scoring control samples. Using 17 fullrepeats as the call criterion value, one can rule out the 7^(th) highestscoring sample. The call criterion value may be adjusted based onvarious considerations such as the needs for sensitivity andselectivity.

As shown in Table 2, haploid repeat expectations vary based on differenttriplet length. For instance, the expected number of reads fully in therepeat sequence of 60 triplets is 12 under the given experimentalconditions. These reads fully in the repeat sequence would constitutehigh-count reads in the analysis presented herein. If a test sample hasso many more high-count reads than this expected value that the sample'snumber of high-count reads fall outside of a distribution of controlsamples (whose repeat sequence has 60 triplets), one may infer that thetest sample has a repeat sequence longer than 60 triplets (i.e., arepeat expansion). Therefore, it is possible to obtain a threshold forcalling a repeat expansion based on a distribution of control samples'high-count read. FIG. 14 shows the theoretical simulated distribution ofthe expected number of reads fully within a repeat sequence of 60triplets. Shown on the x-axis is the number of reads fully in the repeatsequence. The y-axis indicates the percentage of samples having aparticular number of reads. The left vertical line indicates the 5^(th)percentile and the right vertical line the 95^(th) percentile withregard to the number of reads. Therefore, 90% of the samples having a 60triplet repeat sequence would fall in the range between the two verticallines in terms of the number of reads fully in the repeat sequence. Onecan use this distribution to assign confidence intervals to call arepeat expansion. For instance, one can set 19 as the threshold to calla repeat expansion having more than 60 triplets, and the confidenceinterval would be higher than 95%.

FIG. 15 shows the mean, 5^(th) percentile and 95^(th) percentile ofexpected number of reads fully in the repeat sequence having variousnumbers of triplets based on simulations with the same experimentalconditions of FIG. 14. FIG. 15 includes the relevant data points of FIG.14 and expands them to repeat sequences having a range of triplet repeatcounts. Shown on the x-axis is the number of triplet repeat counts.Shown on the y-axis is the number of reads fully in the repeat sequence.The mean is shown as a solid line, and the 5^(th) percentile and 95^(th)percentile are shown as dashed lines flanking the mean. The verticalline indicates repeat triplet counts of 60, corresponding to the repeattriplet count of FIG. 14. In some implementations, the 95^(th)percentile values may be used to call for a repeat expansion above theindicated repeat counts. For instance, a criterion of about 40 readsfully in the repeat sequence can be set to call an expansion of a100-triplet repeat sequence.

FIG. 16 shows the same simulated data as FIG. 15, while identifying theobservation of having 20 reads fully in the repeat sequence. The figureshows that a 95% of samples having a repeat sequence of 61 tripletswould have less than 20 reads fully in the repeat. In other words, onemay call a repeat expansion beyond 61 triplet repeats with 95%confidence when 20 reads are observed as being fully in the repeat.Moreover, 5% of samples having a repeat sequence of 92 triplets wouldhave more than 20 reads fully in the repeat.

EXAMPLE 2 Determining Repeat Expansion Related to ALS

This example presents data for amyotrophic lateral sclerosis (ALS)patients that were analyzed in the same manner as that described inExample 1. In a fair percentage of patients, familial ALS involves ahexanucleotide repeat expansion of the nucleotides GGGGCC in the C9ofr72gene located on the short arm of chromosome 9 open reading frame 72.

The analysis involved in FIG. 17 is comparable to the analysis in FIG.13. FIG. 17 shows the numbers of samples having various numbers ofhigh-count reads, where a high-count read is a read containing more than13 copies of hexanucleotides GGGGCC. The control samples are shown bysolid gray bars, and patient samples by hatched bars. Most of thecontrol samples have very few high-count reads. Indeed, more than 96% ofthe control samples have zero high-count reads. Note that the leftmostbar is truncated in the figure. One familial ALS patient has 24high-count reads, and another (not shown in figure) has 35 high-countreads.

This example demonstrates that the methods disclosed herein may be usedto effectively detect repeat expansion in ALS patients.

The present disclosure may be embodied in other specific forms withoutdeparting from its spirit or essential characteristics. The describedembodiments are to be considered in all respects only as illustrativeand not restrictive. The scope of the disclosure is, therefore,indicated by the appended claims rather than by the foregoingdescription. All changes which come within the meaning and range ofequivalency of the claims are to be embraced within their scope.

What is claimed is:
 1. A method for determining the presence or absenceof a repeat expansion of a repeat sequence in a test sample comprisingnucleic acids, wherein the repeat sequence comprises repeats of a repeatunit of nucleotides, the method comprising: (a) obtaining paired endreads of the test sample, wherein the paired end reads have beenprocessed to align to a reference sequence comprising the repeatsequence; (b) identifying anchor and anchored reads in the paired endreads, wherein the anchor reads are reads aligned to or near the repeatsequence, and the anchored reads are unaligned reads that are pairedwith the anchor reads; and (c) determining if the repeat expansion islikely to be present in the test sample based at least in part on theidentified anchored reads.
 2. The method of claim 1, wherein (c)comprises determining if the repeat expansion is likely to be present inthe test sample based on the identified anchor reads and the identifiedanchored reads.
 3. The method of any of the preceding claims, wherein(c) comprises determining if the repeat expansion is likely to bepresent in the test sample based on numbers of repeats of the repeatunit in the identified reads.
 4. The method of claim 3, wherein (c)comprises: obtaining the number of identified reads that are high-countreads, wherein the high-count reads comprise reads having more repeatsthan a threshold value; and comparing the number of high-count reads inthe test sample to a call criterion.
 5. The method of claim 4, whereinthe threshold value for high-count reads is at least about 80% of themaximum number of repeats, which maximum is calculated from the lengthof the paired end reads and the length of the repeat unit.
 6. The methodof claim 5, wherein the threshold value for high-count reads is at leastabout 90% of the maximum number of repeats.
 7. The method of claim 4,wherein the call criterion is obtained from a distribution of high-countreads of control samples.
 8. The method of claim 4, wherein the callcriterion is calculated from the length of the paired end reads, alength of a sequence having the repeat expansion, and a sequencingdepth.
 9. The method of claim 4, wherein the call criterion iscalculated from the distance between the first and last observation ofthe repeat sequence within the reads.
 10. The method of any of thepreceding claims, wherein the anchor reads are aligned to or withinabout 5 kb of the repeat sequence.
 11. The method of any of thepreceding claims, wherein the anchor reads are aligned to or withinabout 1 kb of the repeat sequence.
 12. The method of any of thepreceding claims, wherein the unaligned reads comprise reads that cannotbe aligned or are poorly aligned to the reference sequence.
 13. Themethod of any of the preceding claims, wherein the reference sequencecomprises a reference genome.
 14. The method of any of the precedingclaims, further comprising determining that the individual from whom thetest sample is obtained has an elevated risk of one of Fragile Xsyndrome, amyotrophic lateral sclerosis (ALS), Huntington's disease,Friedreich's ataxia, spinocerebellar ataxia, spino-bulbar muscularatrophy, myotonic dystrophy, Machado-Joseph disease, or dentatorubralpallidoluysian atrophy.
 15. The method of any of the preceding claims,wherein (c) comprises comparing a distribution of the numbers of repeatsof the repeat unit in the identified reads for the test sample and adistribution of numbers of repeats for one or more control samples. 16.The method of claim 15, wherein comparing the distribution for the testsample to the distribution for the control samples comprises using aMann-Whitney rank test to determine if the distribution of the testsample statistically significantly differs from the distribution of thecontrol samples.
 17. The method of claim 16, further comprisingdetermining that the repeat expansion is likely present in the testsample if the test sample's distribution is skewed more towards highernumbers of repeats than the control samples, and the p value for theMann-Whitney rank test is smaller than about 0.0001.
 18. The method ofclaim 17, further comprising determining that the repeat expansion islikely present in the test sample if the test sample's distribution isskewed more towards higher numbers of repeats than the control samples,and the p value for the Mann-Whitney rank test is smaller than about0.00001.
 19. The method of any of the preceding claims, wherein thenumbers of repeats are numbers of in-frame repeats.
 20. The method ofany of the preceding claims, further comprising using a sequencer togenerate paired end reads from the test sample.
 21. The method of any ofthe preceding claims, further comprising extracting the test sample froman individual.
 22. The method of any of the preceding claims, whereinthe test sample is a blood sample, a urine sample, a saliva sample, or atissue sample.
 23. The method of any of the preceding claims, whereinthe test sample comprises fetal and maternal cell-free nucleic acids.24. The method of any of the preceding claims, wherein the repeat unitcomprises 2 to 50 nucleotides.
 25. The method of any of the precedingclaims, wherein the paired end reads are shorter than a repeat sequencehaving the repeat expansion.
 26. The method of claim 25, wherein thepaired end reads comprise reads of about 20 bp to 1000 bp.
 27. Themethod of claim 26, wherein the paired end reads comprise reads of about50 bp to 500 bp.
 28. The method of any of claim 27, wherein the pairedend reads comprise reads of about 80 by to 150 bp.
 29. The method ofclaim 25, wherein a sequence having the repeat expansion is longer thanabout 100 bp.
 30. The method of claim 29, wherein the sequence havingthe repeat expansion is longer than about 500 bp.
 31. The method ofclaim 30, wherein the sequence having the repeat expansion is longerthan about 1000 bp.
 32. The method of any of the preceding claims,wherein the paired end reads are obtained from inserts of about 100-5000bp.
 33. The method of claim 32, wherein the inserts are about 100-1000bp long.
 34. The method of claim 32, wherein the inserts are about1000-5000 bp long.
 35. A method for detecting a repeat expansion in atest sample comprising nucleic acids, the method comprising: (a)obtaining paired end reads of the test sample; (b) aligning the pairedend reads to a reference genome; (c) identifying unaligned reads fromthe whole genome, wherein the unaligned reads comprise paired end readsthat cannot be aligned or are poorly aligned to the reference sequence;and (d) analyzing the numbers of repeats of a repeat unit in theunaligned reads to determine if a repeat expansion is likely present inthe test sample.
 36. The method of claim 35, wherein analyzing thenumbers of repeats of the repeat unit in the unaligned reads comprises:obtaining the number of high-count reads, wherein the high-count readscomprise unaligned reads having more repeats than a threshold value; andcomparing the number of high-count reads in the test sample to a callcriterion.
 37. The method of claim 36, wherein the threshold value forhigh-count reads is at least about 80% of the maximum number of repeats,which maximum is calculated as the ratio of the length of the paired endreads over the length of the repeat unit.
 38. The method of claim 36,wherein the high-count reads further comprise reads that are paired tothe unaligned reads and have more repeats than the threshold value. 39.The method of any of claims 35-38, further comprising, upondetermination that the repeat expansion is likely present in the testsample, performing an additional analysis to determining if the testsample comprises a repeat expansion of a particular repeat sequence ofinterest.
 40. The method of claim 39, wherein the additional analysiscomprises assaying the test sample using reads longer than the pairedend reads.
 41. The method of claim 40, wherein the additional analysiscomprises assaying the test sample using single molecule sequencing orsynthetic long-read sequencing.
 42. The method of claim 39, furthercomprising, prior to performing the additional analysis, identifyingpaired end reads that are paired to the unaligned reads and are alignedto or near a repeat sequence on the reference genome; and providing therepeat sequence as the particular repeat sequence of interest.
 43. Themethod of claim 42, wherein the additional analysis comprises ananalysis using the method of any of claims 1 to
 34. 44. A system fordetermining the presence or absence of a repeat expansion of a repeatsequence in a test sample comprising nucleic acids, wherein the repeatsequence comprises repeats of a repeat unit, the method comprising: asequencer for sequencing nucleic acids of the test sample; a processor;and one or more computer-readable storage media having stored thereoninstructions for execution on said processor to evaluate copy number inthe test sample by: (a) aligning paired end reads to a referencesequence comprising the repeat sequence; (b) identifying anchor andanchored reads in the paired end reads, wherein the anchor reads arereads aligned to or near the repeat sequence, and the anchored reads areunaligned reads that are paired with the anchor reads; and (c)determining if the repeat expansion is likely to be present in the testsample based at least in part on the identified anchored reads.
 45. Thesystem of claim 44, wherein (c) comprises determining if the repeatexpansion is likely to be present in the test sample based at least inpart on the numbers of repeats of the repeat unit in the identifiedreads.
 46. The system of claim 45, wherein (c) comprises: obtaining thenumber of identified reads that are high-count reads, wherein thehigh-count reads comprise reads having more repeats than a thresholdvalue; and comparing the number of high-count reads in the test sampleto a call criterion.
 47. A computer program product comprising anon-transitory machine readable medium storing program code that, whenexecuted by one or more processors of a computer system, causes thecomputer system to implement a method for identifying a repeat expansionof a repeat sequence in a test sample comprising nucleic acids, whereinthe repeat sequence comprises repeats of a repeat unit of nucleotides,said program code comprising: (a) code for obtaining paired end reads ofthe test sample that have been processed to align to a referencesequence comprising the repeat sequence; (b) code for identifying anchorand anchored reads in the paired end reads, wherein the anchor reads arereads aligned to or near the repeat sequence, and the anchored reads areunaligned reads that are paired with the anchor reads; and (c) code fordetermining if the repeat expansion is likely to be present in the testsample based at least in part on the identified anchored reads.
 48. Thecomputer program product of claim 47, wherein (c) comprises code foranalyzing the numbers of repeats of the repeat unit in the identifiedreads.
 49. The computer program product of claim 48, wherein (c)comprises: code for obtaining the number identified reads that arehigh-count reads, wherein the high-count reads comprise reads havingmore repeats than a threshold value; and code for comparing the numberof high-count reads in the test sample to a call criterion.